An update on Drug Repurposing: Re-written saga of the drug’s fate

Low-dose naltrexone extends healthspan and lifespan in C. elegans via SKN-1 activation

As the global aging population rises, finding effective interventions to improve aging health is crucial. Drug repurposing, utilizing existing drugs for new purposes, presents a promising strategy for rapid implementation. We explored naltrexone from the Library of Integrated Network-based Cellular Signatures (LINCS) based on several selection criteria. Low-dose naltrexone (LDN) has gained attention for treating various diseases, yet its impact on longevity remains underexplored. Our study on C. elegans demonstrated that a low dose, but not high dose, of naltrexone extended the healthspan and lifespan. This effect was mediated through SKN-1 (NRF2 in mammals) signaling, influencing innate immune gene expression and upregulating oxidative stress responses. With LDN's low side effects profile, our findings underscore its potential as a geroprotector, suggesting further exploration for promoting healthy aging in humans is warranted.

Computational biology approaches for drug repurposing

The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.

Innovative strategies for measuring kinase activity to accelerate the next wave of novel kinase inhibitors

The development of protein kinase inhibitors (PKIs) has gained significance owing to their therapeutic potential for diseases like cancer. In addition, there has been a rise in refining kinase activity assays, each possessing unique biological and analytical characteristics crucial for PKI development. However, the PKI development pipeline experiences high attrition rates and approved PKIs exhibit unexploited potential because of variable patient responses. Enhancing PKI development efficiency involves addressing challenges related to understanding the PKI mechanism of action and employing biomarkers for precision medicine. Selecting appropriate kinase activity assays for these challenges can overcome these attrition rate issues. This review delves into the current obstacles in kinase inhibitor development and elucidates kinase activity assays that can provide solutions.

Solid Lipid Nanoparticles Enhancing the Leishmanicidal Activity of Delamanid

Leishmaniasis, a zoonotic parasitic disease transmitted by infected sandflies, impacts nearly 1 million people yearly and is endemic in many countries across Asia, Africa, the Americas, and the Mediterranean; despite this, it remains a neglected disease with limited effective treatments, particularly in impoverished communities with limited access to healthcare. This study aims to repurpose approved drugs for an affordable leishmaniasis treatment. After the screening of potential drug candidates by reviewing databases and utilizing molecular docking analysis, delamanid was chosen to be incorporated into solid lipid nanoparticles (SLNPs). Both in cellulo and in vivo tests confirmed the successful payload release within macrophages and through the epidermis following topical application on murine skin. The evaluation of macrophages infected with L. infantum amastigotes showed that the encapsulated delamanid exhibited greater leishmanicidal activity compared with the free drug. The process of encapsulating delamanid in SLNPs, as demonstrated in this study, places a strong emphasis on employing minimal technology, ensuring energy efficiency, cost-effectiveness, and reproducibility. It enables consistent, low-cost production of nanomedicines, even on a small scale, offering a promising step toward more accessible and effective leishmaniasis treatments.

A computational study of potent series of selective estrogen receptor degraders for breast cancer therapy

A detailed multistep framework combining quantitative structure-activity relationship, global reactivity, absorption, distribution, metabolism and elimination properties, molecular docking and molecular dynamics simulation (MD) on a series of Selective Estrogen Receptor Down-Regulators (SERDs) interacting with Estrogen Receptor α (ERα) has been performed. The partial least squares regression method derived an empirical model with better predictive capability. The results of global reactivity descriptors revealed that all the compounds are considered strong electrophiles, allowing them to participate in polar reactions more easily. The Brain Or IntestinaL EstimateD permeation diagram revealed that compounds 49 and 31 were predicted to be well absorbed by the human gastrointestinal tract and would not enter the brain. The elucidation of the binding mode between the most active compounds that comply with Lipinski's and Veber's rules from the dataset and ERα targets was explored by molecular docking. The MD simulations were performed for 100 ns on the best compounds, which indicated their stability state under dynamics simulations. These findings are expected to help predict the anticancer activities of the studied SERD compounds and better understand their binding mechanism with ERα targets.Communicated by Ramaswamy H. Sarma.

Repurposing approved non-oncology drugs for cancer therapy: a comprehensive review of mechanisms, efficacy, and clinical prospects

Cancer poses a significant global health challenge, with predictions of increasing prevalence in the coming years due to limited prevention, late diagnosis, and inadequate success with current therapies. In addition, the high cost of new anti-cancer drugs creates barriers in meeting the medical needs of cancer patients, especially in developing countries. The lengthy and costly process of developing novel drugs further hinders drug discovery and clinical implementation. Therefore, there has been a growing interest in repurposing approved drugs for other diseases to address the urgent need for effective cancer treatments. The aim of this comprehensive review is to provide an overview of the potential of approved non-oncology drugs as therapeutic options for cancer treatment. These drugs come from various chemotherapeutic classes, including antimalarials, antibiotics, antivirals, anti-inflammatory drugs, and antifungals, and have demonstrated significant antiproliferative, pro-apoptotic, immunomodulatory, and antimetastatic properties. A systematic review of the literature was conducted to identify relevant studies on the repurposing of approved non-oncology drugs for cancer therapy. Various electronic databases, such as PubMed, Scopus, and Google Scholar, were searched using appropriate keywords. Studies focusing on the therapeutic potential, mechanisms of action, efficacy, and clinical prospects of repurposed drugs in cancer treatment were included in the analysis. The review highlights the promising outcomes of repurposing approved non-oncology drugs for cancer therapy. Drugs belonging to different therapeutic classes have demonstrated notable antitumor effects, including inhibiting cell proliferation, promoting apoptosis, modulating the immune response, and suppressing metastasis. These findings suggest the potential of these repurposed drugs as effective therapeutic approaches in cancer treatment. Repurposing approved non-oncology drugs provides a promising strategy for addressing the urgent need for effective and accessible cancer treatments. The diverse classes of repurposed drugs, with their demonstrated antiproliferative, pro-apoptotic, immunomodulatory, and antimetastatic properties, offer new avenues for cancer therapy. Further research and clinical trials are warranted to explore the full potential of these repurposed drugs and optimize their use in treating various cancer types. Repurposing approved drugs can significantly expedite the process of identifying effective treatments and improve patient outcomes in a cost-effective manner.

ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning

The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

Current and emerging prospects in the psoriatic treatment

Psoriasis is an autoimmune chronic disorder that causes inflammation and a scaly epidermis. The exact pathogenesis of the disease is not known yet. According to the studies, psoriasis is considered an immune-mediated disease. Until now it is believed that genetic and environmental factors are responsible for the disease. There are many comorbidities associated with psoriasis which increases difficulties as patients in some cases get addicted to drugs, alcohol, and smoking which reduces their quality of life. The patient may face social ignorance or suicidal thoughts which may arise in the patient's mind. Due to the undefined trigger of the disease, the treatment is not fully established but by considering the severe impact of the disease researchers are focusing on novel approaches for successful treatment. which has succeeded to a large extent. Here we review pathogenesis, problems faced by psoriatic patients, the need for the development of new treatments over conventional therapies, and the history of psoriatic treatments. We thoroughly focus on emerging treatments like biologics, biosimilars, and small molecules which are now showing more efficacy and safety than conventional treatments. Also, this review article discusses novel approaches which are now in research such as drug repurposing, treatment by stimulation of the vagus nerve, regulation of microbiota, and autophagy for improving disease conditions.

Pharmacological updates of nifuroxazide: Promising preclinical effects and the underlying molecular mechanisms

Nifuroxazide (NFX) is a safe nitrofuran antibacterial drug used clinically to treat acute diarrhea and infectious traveler diarrhea or colitis. Recent studies revealed that NFX displays multiple pharmacological effects, including anticancer, antioxidant, and anti-inflammatory effects. NFX has potential roles in inhibiting thyroid, breast, lung, bladder, liver, and colon cancers and osteosarcoma, melanoma, and others mediated by suppressing STAT3 as well as ALDH1, MMP2, MMP9, Bcl2 and upregulating Bax. Moreover, it has promising effects against sepsis-induced organ injury, hepatic disorders, diabetic nephropathy, ulcerative colitis, and immune disorders. These promising effects appear to be mediated by suppressing STAT3 as well as NF-κB, TLR4, and β-catenin expressions and effectively decreasing downstream cytokines TNF-α, IL-1β, and IL-6. Our review summarizes the available studies on the molecular biological mechanisms of NFX in cancer and other diseases and it is recommended to translate the studies in experimental animals and cultured cells and repurpose NFX in various diseases for scientific evidence based on human studies.

Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease

SARS-CoV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdowns, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure COVID-19. In the first step, a total of 133 drug-likeness bioactive molecules are retrieved from the ChEMBL database against SARS coronavirus 3CL Protease. Based on the standard IC50, the dataset is divided into three classes: active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR)-based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting-, XGBoost-, Support Vector-, Decision Tree-, and Random Forest-based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with the ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134, and 426898. These molecules are highly suitable drug candidates for SARS-CoV-2 3CL Protease. In the next step, the efficacy of the bioactive molecules is computed in terms of binding affinity using molecular docking, and then six bioactive molecules are shortlisted, with the ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-CoV-2. It is anticipated that the pharmacologist and/or drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-CoV-2. They can adopt these promising compounds for their downstream drug development stages.

EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers

The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing databases transformed the landscape of cancer research, but most of them are difficult and time-consuming for casual users. Furthermore, to conduct systematic searches and data retrieval on multiple targets, researchers need the help of an expert bioinformatician, who is not always readily available for smaller research teams. We invite research teams to join and aim to enhance the cooperative work of more experienced groups to harmonize international efforts to overcome devastating malignancies. Here, we integrate available fundamental data and present a novel, open access, data-aggregating, drug repurposing platform, deriving our searches from the entries of Clue.io. We show how we integrated our previous expertise in small-cell lung cancer (SCLC) to initiate a new platform to overcome highly progressive cancers such as triple-negative breast and pancreatic cancer with data-aggregating approaches. Through the front end, the current content of the platform can be further expanded or replaced and users can create their drug-target list to select the clinically most relevant targets for further functional validation assays or drug trials. EZCancerTarget integrates searches from publicly available databases, such as PubChem, DrugBank, PubMed, and EMA, citing up-to-date and relevant literature of every target. Moreover, information on compounds is complemented with biological background information on eligible targets using entities like UniProt, String, and GeneCards, presenting relevant pathways, molecular- and biological function and subcellular localizations of these molecules. Cancer drug discovery requires a convergence of complex, often disparate fields. We present a simple, transparent, and user-friendly drug repurposing software to facilitate the efforts of research groups in the field of cancer research.

Crosstalk between neurological, cardiovascular, and lifestyle disorders: insulin and lipoproteins in the lead role

Insulin resistance and impaired lipoprotein metabolism contribute to a plethora of metabolic and cardiovascular disorders. These alterations have been extensively linked with poor lifestyle choices, such as consumption of a high-fat diet, smoking, stress, and a redundant lifestyle. Moreover, these are also known to increase the co-morbidity of diseases like Type 2 diabetes mellitus and atherosclerosis. Under normal physiological conditions, insulin and lipoproteins exert a neuroprotective role in the central nervous system. However, the tripping of balance between the periphery and center may alter the normal functioning of the brain and lead to neurological disorders such as Alzheimer's disease, Parkinson's disease, stroke, depression, and multiple sclerosis. These neurological disorders are further characterized by certain behavioral and molecular changes that show consistent overlap with alteration in insulin and lipoprotein signaling pathways. Therefore, targeting these two mechanisms not only reveals a way to manage the co-morbidities associated with the circle of the metabolic, central nervous system, and cardiovascular disorders but also exclusively work as a disease-modifying therapy for neurological disorders. In this review, we summarize the role of insulin resistance and lipoproteins in the progression of various neurological conditions and discuss the therapeutic options currently in the clinical pipeline targeting these two mechanisms; in addition, challenges faced in designing these therapeutic approaches have also been touched upon briefly.

Screening and validation of platelet activation-related lncRNAs as potential biomarkers for prognosis and immunotherapy in gastric cancer patients

Background: Platelets (PLT) have a significant effect in promoting cancer progression and hematogenous metastasis. However, the effect of platelet activation-related lncRNAs (PLT-related lncRNAs) in gastric cancer (GC) is still poorly understood. In this study, we screened and validated PLT-related lncRNAs as potential biomarkers for prognosis and immunotherapy in GC patients.

Methods: We obtained relevant datasets from the Cancer Genome Atlas (TCGA) and Gene Ontology (GO) Resource Database. Pearson correlation analysis was used to identify PLT-related lncRNAs. By using the univariate, least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we constructed the PLT-related lncRNAs model. Kaplan-Meier survival analysis, univariate, multivariate Cox regression analysis, and nomogram were used to verify the model. The Gene Set Enrichment Analysis (GSEA), drug screening, tumor immune microenvironment analysis, epithelial-mesenchymal transition (EMT), and DNA methylation regulators correlation analysis were performed in the high- and low-risk groups. Patients were regrouped based on the risk model, and candidate compounds and immunotherapeutic responses aimed at GC subgroups were also identified. The expression of seven PLT-related lncRNAs was validated in clinical medical samples using quantitative reverse transcription-polymerase chain reaction (qRT-PCR).

Results: In this study, a risk prediction model was established using seven PLT-related lncRNAs -(AL355574.1, LINC01697, AC002401.4, AC129507.1, AL513123.1, LINC01094, and AL356417.2), whose expression were validated in GC patients. Kaplan-Meier survival analysis, the receiver operating characteristic (ROC) curve analysis, univariate, multivariate Cox regression analysis verified the accuracy of the model. We screened multiple targeted drugs for the high-risk patients. Patients in the high-risk group had a poorer prognosis since low infiltration of immune killer cells, activation of immunosuppressive pathways, and poor response to immunotherapy. In addition, we revealed a close relationship between risk scores and EMT and DNA methylation regulators. The nomogram based on risk score suggested a good ability to predict prognosis and high clinical benefits.

Conclusion: Our findings provide new insights into how PLT-related lncRNAs biomarkers affect prognosis and immunotherapy. Also, these lncRNAs may become potential biomarkers and therapeutic targets for GC patients.

Identification of anthelmintic parbendazole as a therapeutic molecule for HNSCC through connectivity map-based drug repositioning

Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers; however, its outcome of pharmacotherapy is always very limited. Herein, we performed a batch query in the connectivity map (cMap) based on bioinformatics, queried out 35 compounds with therapeutic potential, and screened out parbendazole as a most promising compound, which had an excellent inhibitory effect on the proliferation of HNSCC cell lines. In addition, tubulin was identified as a primary target of parbendazole, and the direct binding between them was further verified. Parbendazole was further proved as an effective tubulin polymerization inhibitor, which can block the cell cycle, cause apoptosis and prevent cell migration, and it exhibited reasonable therapeutic effect and low toxicity in the in vivo and in vitro anti-tumor evaluation. Our study repositioned an anthelmintic parbendazole to treat HNSCC, which revealed a therapeutic utility and provided a new treatment option for human cancers.

Enhancing autophagy in Alzheimer's disease through drug repositioning

Alzheimer's disease (AD) is one of the biggest human health threats due to increases in aging of the global population. Unfortunately, drugs for treating AD have been largely ineffective. Interestingly, downregulation of macroautophagy (autophagy) plays an essential role in AD pathogenesis. Therefore, targeting autophagy has drawn considerable attention as a therapeutic approach for the treatment of AD. However, developing new therapeutics is time-consuming and requires huge investments. One of the strategies currently under consideration for many diseases is "drug repositioning" or "drug repurposing". In this comprehensive review, we have provided an overview of the impact of autophagy on AD pathophysiology, reviewed the therapeutics that upregulate autophagy and are currently used in the treatment of other diseases, including cancers, and evaluated their repurposing as a possible treatment option for AD. In addition, we discussed the potential of applying nano-drug delivery to neurodegenerative diseases, such as AD, to overcome the challenge of crossing the blood brain barrier and specifically target molecules/pathways of interest with minimal side effects.

Drug Repurposing to Target Neuroinflammation and Sensory Neuron-Dependent Pain

Around 20% of the American population have chronic pain and estimates in other Western countries report similar numbers. This represents a major challenge for global health care systems. Additional problems for the treatment of chronic and persistent pain are the comparably low efficacy of existing therapies, the failure to translate effects observed in preclinical pain models to human patients and related setbacks in clinical trials from previous attempts to develop novel analgesics. Drug repurposing offers an alternative approach to identify novel analgesics as it can bypass various steps of classical drug development. In recent years, several approved drugs were attributed analgesic properties. Here, we review available data and discuss recent findings suggesting that the approved drugs minocycline, fingolimod, pioglitazone, nilotinib, telmisartan, and others, which were originally developed for the treatment of different pathologies, can have analgesic, antihyperalgesic, or neuroprotective effects in preclinical and clinical models of inflammatory or neuropathic pain. For our analysis, we subdivide the drugs into substances that can target neuroinflammation or substances that can act on peripheral sensory neurons, and highlight the proposed mechanisms. Finally, we discuss the merits and challenges of drug repurposing for the development of novel analgesics.

Therapeutic dilemmas in addressing SARS-CoV-2 infection: Favipiravir versus Remdesivir

Coronavirus disease 2019 (COVID-19) represents an unmet clinical need, due to a high mortality rate, rapid mutation rate in the virus, increased chances of reinfection, lack of effectiveness of repurposed drugs and economic damage. COVID-19 pandemic has created an urgent need for effective molecules. Clinically proven efficacy and safety profiles have made favipiravir (FVP) and remdesivir (RDV) promising therapeutic options for use against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Even though both are prodrug molecules with an antiviral role based on a similar mechanism of action, differences in pharmacological, pharmacokinetic and pharmacotoxicological mechanisms have been identified. The present study aims to provide a comprehensive comparative assessment of FVP and RDV against SARS-CoV-2 infections, by centralizing medical data provided by significant literature and authorized clinical trials, focusing on the importance of a better understanding of the interactions between drug molecules and infectious agents in order to improve the global management of COVID-19 patients and to reduce the risk of antiviral resistance.

Dissecting the Mechanism of Action of Spiperone-A Candidate for Drug Repurposing for Colorectal Cancer

Simple Summary

Despite advances in primary and adjuvant treatments, approximately 50% of colorectal cancer (CRC) patients still die from recurrence and metastatic disease. Thus, alternative and more effective therapeutic approaches are expected to be developed. Drug repurposing is increasing interest in cancer therapy, as it represents a cheaper and faster alternative strategy to de novo drug synthesis. Psychiatric medications are promising as a new generation of antitumor drugs. Here, we demonstrate that spiperone—a licensed drug for the treatment of schizophrenia—induces apoptosis in CRC cells. Our data reveal that spiperone’s cytotoxicity in CRC cells is mediated by phospholipase C activation, intracellular calcium homeostasis dysregulation, and irreversible endoplasmic reticulum stress induction, resulting in lipid metabolism alteration and Golgi apparatus damage. By identifying new targetable pathways in CRC cells, our findings represent a promising starting point for the design of novel therapeutic strategies for CRC.

Abstract

Approximately 50% of colorectal cancer (CRC) patients still die from recurrence and metastatic disease, highlighting the need for novel therapeutic strategies. Drug repurposing is attracting increasing attention because, compared to traditional de novo drug discovery processes, it may reduce drug development periods and costs. Epidemiological and preclinical evidence support the antitumor activity of antipsychotic drugs. Herein, we dissect the mechanism of action of the typical antipsychotic spiperone in CRC. Spiperone can reduce the clonogenic potential of stem-like CRC cells (CRC-SCs) and induce cell cycle arrest and apoptosis, in both differentiated and CRC-SCs, at clinically relevant concentrations whose toxicity is negligible for non-neoplastic cells. Analysis of intracellular Ca²⁺ kinetics upon spiperone treatment revealed a massive phospholipase C (PLC)-dependent endoplasmic reticulum (ER) Ca²⁺ release, resulting in ER Ca²⁺ homeostasis disruption. RNA sequencing revealed unfolded protein response (UPR) activation, ER stress, and induction of apoptosis, along with IRE1-dependent decay of mRNA (RIDD) activation. Lipidomic analysis showed a significant alteration of lipid profile and, in particular, of sphingolipids. Damage to the Golgi apparatus was also observed. Our data suggest that spiperone can represent an effective drug in the treatment of CRC, and that ER stress induction, along with lipid metabolism alteration, represents effective druggable pathways in CRC.

Repurposing – second life for drugs

Drug repurposing refers to finding new indications for existing drugs. The paradigm shift from traditional drug discovery to drug repurposing is driven by the fact that new drug pipelines are getting dried up because of mounting Research & Development (R&D) costs, long timeline for new drug development, low success rate for new molecular entities, regulatory hurdles coupled with revenue loss from patent expiry and competition from generics. Anaemic drug pipelines along with increasing demand for newer effective, cheaper, safer drugs and unmet medical needs call for new strategies of drug discovery and, drug repurposing seems to be a promising avenue for such endeavours. Drug repurposing strategies have progressed over years from simple serendipitous observations to more complex computational methods in parallel with our ever-growing knowledge on drugs, diseases, protein targets and signalling pathways but still the knowledge is far from complete. Repurposed drugs too have to face many obstacles, although lesser than new drugs, before being successful.

Repurposing old molecules for new indications: Defining pillars of success from lessons in the past

Drug repurposing or studying existing drugs for potential therapeutic utility in newer indications has been identified as an attractive option for treating a number of diseases. Various strategies of drug repurposing include serendipitous observation of drug's unexpected effects, directing the failed investigational drugs to new indications and currently adopted systematic approach to identify, screen and develop existing drug molecules for new off-label indications. Drug repurposing is able to constructively overcome the bottleneck restraints encountered during traditional de novo drug development process in grounds of timelines, cost and resources. However, success rates of drug repurposing programs are not very impressive. Through a meticulous examination of some failed repurposing attempts we aimed to identify key factors leading to high attrition rate in such studies. Based on the fundamental elements of knowledge and evaluation, we have defined four pillars toward improving success rate in drug repurposing programs viz. sound knowledge of the repurposed drug's pharmacological characteristics (pillar 1: drug pharmacology); drug formulation considerations in new indication (pillar 2: drug formulation); evaluation in representative biological assays with translational potential (pillar 3: evaluation in biological assays); and robust clinical trial methodologies including biomarker driven approach to provide conclusive evidence of repurposed drug's efficacy in new indication (pillar 4: clinical evaluation). In addition to the pharmacological challenges, certain regulatory concerns, including lack of clear guidelines for evaluation and market exclusivity pose hurdles in the application of drug repurposing, which may however be overcome to a great extent by adopting some strategies as discussed in this review.

Computational methods directed towards drug repurposing for COVID-19: advantages and limitations

Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic status of countries. Although vaccines are now available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent for COVID-19, it continues to transmit and newer variants of concern have been consistently emerging world-wide. Computational strategies involving drug repurposing offer a viable opportunity to choose a medication from a rundown of affirmed drugs against distinct diseases including COVID-19. While pandemics impede the healthcare systems, drug repurposing or repositioning represents a hopeful approach in which existing drugs can be remodeled and employed to treat newer diseases. In this review, we summarize the diverse computational approaches attempted for developing drugs through drug repurposing or repositioning against COVID-19 and discuss their advantages and limitations. To this end, we have outlined studies that utilized computational techniques such as molecular docking, molecular dynamic simulation, disease-disease association, drug-drug interaction, integrated biological network, artificial intelligence, machine learning and network medicine to accelerate creation of smart and safe drugs against COVID-19.

Application of comprehensive bioinformatics approaches to reconnoiter crucial genes and pathways underpinning hepatocellular carcinoma: a drug repurposing endeavor

Hepatocellular carcinoma (HCC) is the fifth most common neoplasm in the world. Chronic inflammation of liver and associated wound healing processes collectively contribute to the development of cirrhosis which further progresses to dysplastic nodule and then to HCC. Etiological mediators and ongoing manipulations at cellular level in HCC are well established; however, key protein interactions and genetic alterations involved in stepwise hepatocarcinogenic pathways are seldom explored. This study aims to unravel novel targets of HCC and repurpose the FDA-approved drugs against the same. Genetic data pertinent to different stages of HCC were retrieved from GSE6764 dataset and analyzed via GEO2R. Subsequently, protein-protein interaction network analysis of differentially expressed genes was performed to identify the hub genes with significant interaction. Hub genes displaying higher interactions were considered as potential HCC targets and were validated thorough UALCAN and GEPIA databases. These targets were screened against FDA-approved drugs through molecular docking and dynamics simulation studies to capture the drugs with potential activity against HCC. Finally, cytotoxicity of the shortlisted drug was confirmed in vitro by MTT assay. CDC20 was identified as potential druggable target. Docking, binding energy calculations, and dynamic studies revealed significant interaction exhibited by Labetalol with CDC20. Further, in MTT assay, Labetalol demonstrated an IC₅₀ of 200.29 µg/ml in inhibiting the cell growth of HepG2 cell line. In conclusion, this study discloses a series of key genetic underpinnings of HCC and recommends the pertinence of labetalol as a potential repurposable drug against HCC.

Repurposing drugs as novel triple negative breast cancer therapeutics

BACKGROUND

Among all the types of breast cancer (BC), triple negative breast cancer (TNBC) is the most aggressive form having high metastasis and recurrence rate with limited treatment options. Conventional treatments such as chemotherapy and radiotherapy have lots of toxic side effects and also no FDA approved therapies are available till now. Repurposing of old clinically approved drugs towards various targets of TNBC is the new approach with lesser side effects and also leads to successful inexpensive drug development with less time consuming. Medicinal plants containg various phytoconstituents (flavonoids, alkaloids, phenols, essential oils, tanins, glycosides, lactones) plays very crucial role in combating various types of diseases and used in drug development process because of having lesser side effects.

OBJECTIVE

The present review focuses in summarization of various categories of repurposed drugs against multitarget of TNBC and also summarizes the phytochemical categories that targets TNBC singly or in combination with synthetic old drugs.

METHODS

Literature information was collected from various databases such as Pubmed, Web of Science, Scopus and Medline to understand and clarify the role and mechanism of repurposed synthetic drugs and phytoconstituents aginst TNBC by using keywords like "breast cancer", "repurposed drugs", "TNBC" and "phytoconstituents".

RESULTS

Various repurposed drugs and phytochemicals targeting different signaling pathways that exerts their cytotoxic activities on TNBC cells ultimately leads to apoptosis of cells and also lowers the recurrence rate and stops the metastasis process.

CONCLUSION

Inhibitory effects seen in different levels, which provides information and evidences to researchers towards drug developments process and thus further more investigations and researches need to be taken to get the better therapeutic treatment options against TNBC.

A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing

The alarming pandemic situation of Coronavirus infectious disease COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a critical threat to public health. The unexpected outbreak and unrealistic progression of COVID-19 have generated an utmost need to realize promising therapeutic strategies to fight the pandemic. Drug repurposing-an efficient drug discovery technique from approved drugs is an emerging tactic to face the immediate global challenge. It​ offers a time-efficient and cost-effective way to find potential therapeutic agents for the disease. Artificial Intelligence-empowered deep learning models enable the rapid identification of potentially repurposable drug candidates against diseases. This study presents a deep learning ensemble model to prioritize clinically validated anti-viral drugs for their potential efficacy against SARS-CoV-2. The method integrates the similarities of drug chemical structures and virus genome sequences to generate feature vectors. The best combination of features is retrieved by the convolutional neural network in a deep learning manner. The extracted deep features are classified by the extreme gradient boosting classifier to infer potential virus–drug associations. The method could achieve an AUC of 0.8897 with 0.8571 prediction accuracy and 0.8394 sensitivity under the fivefold cross-validation. The experimental results and case studies demonstrate the suggested deep learning ensemble system yields competitive results compared with the state-of-the-art approaches. The top-ranked drugs are released for further wet-lab researches.

Drug repurposing: Misconceptions, challenges, and opportunities for academic researchers

[Figure: see text].

Computer-aided screening for potential TMPRSS2 inhibitors: a combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches

Transmembrane serine protease 2 (TMPRSS2) has been established as one of the host proteins that facilitate entry of coronaviruses into host cells. One of the approaches often employed towards preventing the entry and proliferation of viruses is computer-aided inhibition studies to identify potent compounds that can inhibit activity of viral targets in the host through binding at the active site. In this study, we developed a pharmacophore model of reportedly potent drugs against severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and -2). The model was used to screen the ZINC database for commercially available compounds having similar features with the experimentally tested drugs. The top 3000 compounds retrieved were docked into the active sites of a homology-modelled TMPRSS2. Docking scores of the top binders were validated and the top-ranked compounds were subjected to ADME, Lipinski's and medicinal Chemistry property predictions for druglikeness analyses. Two lead compounds, ZINC64606047 and ZINC05296775, were identified having binding affinities higher than those of the reference inhibitors, favorable interactions with TMPRSS2 active site residues and good ADME and medicinal chemistry properties. Molecular dynamics simulation was used to assess the stability and dynamics of the interactions of these compounds with TMPRSS2. Binding free energy and contribution energy evaluations were determined using MMPBSA method. Analyses of the trajectory dynamics collectively established further that the lead compounds bound and interacted stably with active site residues of TMPRSS2. Nonetheless, experimental studies are needed to further assess the potentials of these compounds as possible therapeutics against coronaviruses.Communicated by Ramaswamy H. Sarma.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Exploring the selectivity of guanine scaffold in anticancer drug development by computational repurposing approach

Drug repurposing is one of the modern techniques used in the drug discovery to find out the new targets for existing drugs. Insilico methods have a major role in this approach. We used 60 FDA approved antiviral drugs reported in the last 50 years to screen against different cancer cell receptors. The thirteen compounds selected after virtual screening are analyzed for their druggability based on ADMET parameters and found the selectivity of guanine derivatives-didanosine, entecavir, acyclovir, valganciclovir, penciclovir, ganciclovir and valacyclovir as suitable candidates. The pharmacophore model, AARR, suggested based on the common feature alignment, shows that the two fused rings as in guanine and two acceptors-one from keto-oxygen (A5) and other from the substituent attached to nitrogen of imidazole ring (A4) give the druggability to the guanine derivatives. The NBO analysis on N9 is indicative of charge distribution from the ring to substituents, which results in delocalization of negative character in most of the ligands. The molecular dynamics simulations also pointed out the importance of guanine scaffold, which stabilizes the ligands inside the binding pocket of the receptor. All these results are indicative of the selectivity of guanine scaffold in anticancer drug development, especially as PARP1 inhibitors in breast, ovarian and prostate cancer. As these seven molecules are already approved by FDA, we can safely go for further preclinical trials.

Just a Reflection: Does Drug Repurposing Perpetuate Sex-Gender Bias in the Safety Profile?

Vaccines constitute a strategy to reduce the burden of COVID-19, but the treatment of COVID-19 is still a challenge. The lack of approved drugs for severe COVID-19 makes repurposing or repositioning of approved drugs a relevant approach because it occurs at lower costs and in a shorter time. Most preclinical and clinical tests, including safety and pharmacokinetic profiles, were already performed. However, infective and inflammatory diseases such as COVID-19 are linked with hypoalbuminemia and downregulation of both phase I and phase II drug-metabolizing enzymes and transporters, which can occur in modifications of pharmacokinetics and consequentially of safety profiles. This appears to occur in a sex- and gender-specific way because of the sex and gender differences present in the immune system and inflammation, which, in turn, reflect on pharmacokinetic parameters. Therefore, to make better decisions about drug dosage regimens and to increases the safety profile in patients suffering from infective and inflammatory diseases such as COVID-19, it is urgently needed to study repurposing or repositioning drugs in men and in women paying attention to pharmacokinetics, especially for those drugs that are previously scarcely evaluated in women.

Identification of Repurpose Drugs by Computational Analysis of Disease-Gene-Drug Associations

Repurposing of marketed drugs to find new indications has become an alternative to circumvent the risk of traditional drug development by its productivity quality. Despite many approaches, computational analysis has great potential to fuel the development of all-rounder drugs to find new classes of medicine for neglected and rare disease. The genes that can explain variations in drug response associated to disease are more important and significant in drug therapeutics necessitate elucidating the relationships of a gene, drug, and disease. The proposed computational analysis facilitates the discovery of knowledge on both target and disease-based relationships from large sources of biomedical literature spread over different platforms. It uses the utility of text mining for automatic extraction of valuable aggregated biomedical entities (disease, gene, and drug) from PubMed to serves as an input to the analysis of association prediction. The top-ranked associations considered for identification of repurposing drugs and also the hidden associations identified using concurrence principle to extrapolate the new relationships. Such findings are reported as novel and contribute to the knowledge base for pharmacogenomics, would immensely support the discovery and progress of novel therapeutic pathways and patient segment biomarkers.

Proteomics and Drug Repurposing in CLL towards Precision Medicine

Simple Summary

Despite continued efforts, the current status of knowledge in CLL molecular pathobiology, diagnosis, prognosis and treatment remains elusive and imprecise. Proteomics approaches combined with advanced bioinformatics and drug repurposing promise to shed light on the complex proteome heterogeneity of CLL patients and mitigate, improve, or even eliminate the knowledge stagnation. In relation to this concept, this review presents a brief overview of all the available proteomics and drug repurposing studies in CLL and suggests the way such studies can be exploited to find effective therapeutic options combined with drug repurposing strategies to adopt and accost a more “precision medicine” spectrum.

Abstract

CLL is a hematological malignancy considered as the most frequent lymphoproliferative disease in the western world. It is characterized by high molecular heterogeneity and despite the available therapeutic options, there are many patient subgroups showing the insufficient effectiveness of disease treatment. The challenge is to investigate the individual molecular characteristics and heterogeneity of these patients. Proteomics analysis is a powerful approach that monitors the constant state of flux operators of genetic information and can unravel the proteome heterogeneity and rewiring into protein pathways in CLL patients. This review essences all the available proteomics studies in CLL and suggests the way these studies can be exploited to find effective therapeutic options combined with drug repurposing approaches. Drug repurposing utilizes all the existing knowledge of the safety and efficacy of FDA-approved or investigational drugs and anticipates drug alignment to crucial CLL therapeutic targets, leading to a better disease outcome. The drug repurposing studies in CLL are also discussed in this review. The next goal involves the integration of proteomics-based drug repurposing in precision medicine, as well as the application of this procedure into clinical practice to predict the most appropriate drugs combination that could ensure therapy and the long-term survival of each CLL patient.

Repurposing of Antimicrobial Agents for Cancer Therapy: What Do We Know?

The substantial costs of clinical trials, the lengthy timelines of new drug discovery and development, along the high attrition rates underscore the need for alternative strategies for finding quickly suitable therapeutics agents. Given that most approved drugs possess more than one target tightly linked to other diseases, it encourages promptly testing these drugs in patients. Over the past decades, this has led to considerable attention for drug repurposing, which relies on identifying new uses for approved or investigational drugs outside the scope of the original medical indication. The known safety of approved drugs minimizes the possibility of failure for adverse toxicology, making them attractive de-risked compounds for new applications with potentially lower overall development costs and shorter development timelines. This latter case is an exciting opportunity, specifically in oncology, due to increased resistance towards the current therapies. Indeed, a large body of evidence shows that a wealth of non-cancer drugs has beneficial effects against cancer. Interestingly, 335 drugs are currently being evaluated in different clinical trials for their potential activities against various cancers (Redo database). This review aims to provide an extensive discussion about the anti-cancer activities exerted by antimicrobial agents and presents information about their mechanism(s) of action and stage of development/evaluation.

Drug repurposing: Iron in the fire for older drugs

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.

Repurposing Drugs to Treat Heart and Brain Illness

Drug development is a complicated, slow and expensive process with high failure rates. One strategy to mitigate these factors is to recycle existing drugs with viable safety profiles and have gained Food and Drug Administration approval following extensive clinical trials. Cardiovascular and neurodegenerative diseases are difficult to treat, and there exist few effective therapeutics, necessitating the development of new, more efficacious drugs. Recent scientific studies have led to a mechanistic understanding of heart and brain disease progression, which has led researchers to assess myriad drugs for their potential as pharmacological treatments for these ailments. The focus of this review is to survey strategies for the selection of drug repurposing candidates and provide representative case studies where drug repurposing strategies were used to discover therapeutics for cardiovascular and neurodegenerative diseases, with a focus on anti-inflammatory processes where new drug alternatives are needed.

Drug Repurposing: A Strategy for Discovering Inhibitors against Emerging Viral Infections

BACKGROUND

Viral diseases are responsible for several deaths around the world. Over the past few years, the world has seen several outbreaks caused by viral diseases that, for a long time, seemed to possess no risk. These are diseases that have been forgotten for a long time and, until nowadays, there are no approved drugs or vaccines, leading the pharmaceutical industry and several research groups to run out of time in the search for new pharmacological treatments or prevention methods. In this context, drug repurposing proves to be a fast and economically viable technique, considering the fact that it uses drugs that have a well-established safety profile. Thus, in this review, we present the main advances in drug repurposing and their benefit for searching new treatments against emerging viral diseases.

METHODS

We conducted a search in the bibliographic databases (Science Direct, Bentham Science, PubMed, Springer, ACS Publisher, Wiley, and NIH's COVID-19 Portfolio) using the keywords "drug repurposing", "emerging viral infections" and each of the diseases reported here (CoV; ZIKV; DENV; CHIKV; EBOV and MARV) as an inclusion/exclusion criterion. A subjective analysis was performed regarding the quality of the works for inclusion in this manuscript. Thus, the selected works were those that presented drugs repositioned against the emerging viral diseases presented here by means of computational, high-throughput screening or phenotype-based strategies, with no time limit and of relevant scientific value.

RESULTS

291 papers were selected, 24 of which were CHIKV; 52 for ZIKV; 43 for DENV; 35 for EBOV; 10 for MARV; and 56 for CoV and the rest (72 papers) related to the drugs repurposing and emerging viral diseases. Among CoV-related articles, most were published in 2020 (31 papers), updating the current topic. Besides, between the years 2003 - 2005, 10 articles were created, and from 2011 - 2015, there were 7 articles, portraying the outbreaks that occurred at that time. For ZIKV, similar to CoV, most publications were during the period of outbreaks between the years 2016 - 2017 (23 articles). Similarly, most CHIKV (13 papers) and DENV (14 papers) publications occur at the same time interval. For EBOV (13 papers) and MARV (4 papers), they were between the years 2015 - 2016. Through this review, several drugs were highlighted that can be evolved in vivo and clinical trials as possible used against these pathogens showed that remdesivir represent potential treatments against CoV. Furthermore, ribavirin may also be a potential treatment against CHIKV; sofosbuvir against ZIKV; celgosivir against DENV, and favipiravir against EBOV and MARV, representing new hopes against these pathogens.

CONCLUSION

The conclusions of this review manuscript show the potential of the drug repurposing strategy in the discovery of new pharmaceutical products, as from this approach, drugs could be used against emerging viral diseases. Thus, this strategy deserves more attention among research groups and is a promising approach to the discovery of new drugs against emerging viral diseases and also other diseases.

Perspectives on Drug Repurposing

Complex common diseases are a significant burden for our societies and demand not only preventive measures but also more effective, safer, and more affordable treatments. The whole process of the current model of drug discovery and development implies a high investment by the pharmaceutical industry, which ultimately impact in high drug prices. In this sense, drug repurposing would help meet the needs of patients to access useful and novel treatments. Unlike the traditional approach, drug repurposing enters both the preclinical evaluation and clinical trials of the compound of interest faster, budgeting research and development costs, and limiting potential biosafety risks. The participation of government, society, and private investors is needed to secure the funds for experimental design and clinical development of repurposing candidates to have affordable, effective, and safe repurposed drugs. Moreover, extensive advertising of repurposing as a concept in the health community, could reduce prescribing bias when enough clinical evidence exists, which will support the employment of cheaper and more accessible repurposed compounds for common conditions.

A review on drug repurposing applicable to COVID-19

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

Dose prediction for repurposing nitazoxanide in SARS-CoV-2 treatment or chemoprophylaxis

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared a global pandemic and urgent treatment and prevention strategies are needed. Nitazoxanide, an anthelmintic drug, has been shown to exhibit in vitro activity against SARS-CoV-2. The present study used physiologically based pharmacokinetic (PBPK) modelling to inform optimal doses of nitazoxanide capable of maintaining plasma and lung tizoxanide exposures above the reported SARS-CoV-2 EC90 .

METHODS

A whole-body PBPK model was validated against available pharmacokinetic data for healthy individuals receiving single and multiple doses between 500 and 4000 mg with and without food. The validated model was used to predict doses expected to maintain tizoxanide plasma and lung concentrations above the EC90 in >90% of the simulated population. PopDes was used to estimate an optimal sparse sampling strategy for future clinical trials.

RESULTS

The PBPK model was successfully validated against the reported human pharmacokinetics. The model predicted optimal doses of 1200 mg QID, 1600 mg TID and 2900 mg BID in the fasted state and 700 mg QID, 900 mg TID and 1400 mg BID when given with food. For BID regimens an optimal sparse sampling strategy of 0.25, 1, 3 and 12 hours post dose was estimated.

CONCLUSION

The PBPK model predicted tizoxanide concentrations within doses of nitazoxanide already given to humans previously. The reported dosing strategies provide a rational basis for design of clinical trials with nitazoxanide for the treatment or prevention of SARS-CoV-2 infection. A concordant higher dose of nitazoxanide is now planned for investigation in the seamless phase I/IIa AGILE trial.

Exploration of databases and methods supporting drug repurposing: a comprehensive survey

Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

Drug Discovery by Drug Repurposing: Combating COVID-19 in the 21st Century

Due to the rapidly developing nature of the current COVID-19 outbreak and its almost immediate humanitarian and economic toll, coronavirus drug discovery efforts have largely focused on generating potential COVID-19 drug candidates as quickly as possible. Globally, scientists are working day and night to find the best possible solution to treat the deadly virus. During the first few months of 2020, the SARS-CoV-2 outbreak quickly developed into a pandemic, with a mortality rate that was increasing at an exponential rate day by day. As a result, scientists have turned to a drug repurposing approach to rediscover the potential use and benefits of existing approved drugs. Currently, there is no single drug approved by the U.S. Food and Drug Administration (FDA), for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously known as 2019-nCoV) that causes COVID-19. Based on only in-vitro studies, several active drugs are already in the clinical pipeline, made possible by following the compassionate use of medical protocols. This method of repurposing and the use of existing molecules like Remdesivir (GS-5734), Chloroquine, Hydroxychloroquine, etc. has proven to be a landmark in the field of drug rediscovery. In this review article, we will discuss the repurposing of medicines for treating the deadly novel coronavirus (SARS-CoV-2).

Repurposing of Drug Candidates for Treatment of Skin Cancer

Skin cancers are highly prevalent malignancies that affect millions of people worldwide. These include melanomas and nonmelanoma skin cancers. Melanomas are among the most dangerous cancers, while nonmelanoma skin cancers generally exhibit a more benign clinical pattern; however, they may sometimes be aggressive and metastatic. Melanomas typically appear in body regions exposed to the sun, although they may also appear in areas that do not usually get sun exposure. Thus, their development is multifactorial, comprising endogenous and exogenous risk factors. The management of skin cancer depends on the type; it is usually based on surgery, chemotherapy, immunotherapy, and targeted therapy. In this respect, oncological treatments have demonstrated some progress in the last years; however, current therapies still present various disadvantages such as little cell specificity, recurrent relapses, high toxicity, and increased costs. Furthermore, the pursuit of novel medications is expensive, and the authorization for their clinical utilization may take 10-15 years. Thus, repositioning of drugs previously approved and utilized for other diseases has emerged as an excellent alternative. In this mini-review, we aimed to provide an updated overview of drugs' repurposing to treat skin cancer and discuss future perspectives.

Insights into bioinformatic approaches for repurposing compounds as anti-viral drugs

BACKGROUND

Drug repurposing is a cost-effective strategy to identify drugs with novel effects. We searched for drugs exhibiting inhibitory activity to Herpes Simplex virus 1 (HSV-1). Our strategy utilized gene expression data generated from HSV-1-infected cell cultures which was paired with drug effects on gene expression. Gene expression data from HSV-1 infected and uninfected neurons were analyzed using BaseSpace Correlation Engine (Illumina®). Based on the general Signature Reversing Principle (SRP), we hypothesized that the effects of candidate antiviral drugs on gene expression would be diametrically opposite (negatively correlated) to those effects induced by HSV-1 infection.

RESULTS

We initially identified compounds capable of inducing changes in gene expression opposite to those which were consequent to HSV-1 infection. The most promising negatively correlated drugs (Valproic acid, Vorinostat) did not significantly inhibit HSV-1 infection further in African green monkey kidney epithelial cells (Vero cells). Next, we tested Sulforaphane and Menadione which showed effects similar to those caused by viral infections (positively correlated). Intriguingly, Sulforaphane caused a modest but significant inhibition of HSV-1 infection in Vero cells (IC50 = 180.4 µM, p = 0.008), but exhibited toxicity when further explored in human neuronal progenitor cells (NPCs) derived from induced pluripotent stem cells.

CONCLUSIONS

These results reveal the limits of the commonly used SRP strategy when applied to the identification of novel antiviral drugs and highlight the necessity to refine the SRP strategy to increase its utility.

Discovery of a Bradykinin B2 Partial Agonist Profile of Raloxifene in a Drug Repurposing Campaign

Covid-19 urges a deeper understanding of the underlying molecular mechanisms involved in illness progression to provide a prompt therapeutical response with an adequate use of available drugs, including drug repurposing. Recently, it was suggested that a dysregulated bradykinin signaling can trigger the cytokine storm observed in patients with severe Covid-19. In the scope of a drug repurposing campaign undertaken to identify bradykinin antagonists, raloxifene was identified as prospective compound in a virtual screening process. The pharmacodynamics profile of raloxifene towards bradykinin receptors is reported in the present work, showing a weak selective partial agonist profile at the B2 receptor. In view of this new profile, its possible use as a therapeutical agent for the treatment of severe Covid-19 is discussed.

Therapeutic targeting of pancreatic cancer stem cells by dexamethasone modulation of the MKP-1-JNK axis

Postoperative recurrence from microscopic residual disease must be prevented to cure intractable cancers, including pancreatic cancer. Key to this goal is the elimination of cancer stem cells (CSCs) endowed with tumor-initiating capacity and drug resistance. However, current therapeutic strategies capable of accomplishing this are insufficient. Using in vitro models of CSCs and in vivo models of tumor initiation in which CSCs give rise to xenograft tumors, we show that dexamethasone induces expression of MKP-1, a MAPK phosphatase, via glucocorticoid receptor activation, thereby inactivating JNK, which is required for self-renewal and tumor initiation by pancreatic CSCs as well as for their expression of survivin, an anti-apoptotic protein implicated in multidrug resistance. We also demonstrate that systemic administration of clinically relevant doses of dexamethasone together with gemcitabine prevents tumor formation by CSCs in a pancreatic cancer xenograft model. Our study thus provides preclinical evidence for the efficacy of dexamethasone as an adjuvant therapy to prevent postoperative recurrence in patients with pancreatic cancer.

Applications of artificial intelligence in drug development using real-world data

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.