Risk In Drug Development Research Articles

In Silico Screening of FDA‐Approved Anthelmintic Drugs Against LuxR Protein as Quorum‐Sensing Inhibitors and Exploration of Their Binding Mechanism and Electronics Properties

AbstractTreating bacterial infections triggered by multidrug‐resistant bacteria is a global priority. Issue of multidrug resistance can be managed by inhibiting bacterial quorum sensing mechanism. Quorum‐sensing inhibitors (QSIs) are promising alternatives to antibiotics. Herein, a library of FDA‐approved anthelmintic medications was screened using in silico techniques for LuxR protein associated with the quorum‐sensing (QS) mechanism of Staphylococcus aureus. Drug repurposing can reduce the time and financial risk of drug development. Seven FDA‐approved drugs (Fenbendazole, Mebendazole, Flubendazole, Praziquantel, Oxyclozanide, Rafoxanide, and Albendazole) showed strong binding affinity to the LuxR protein with binding free energy less than −6.00 kcal/mol. Molecular dynamic simulation (100 ns) of the docked complexes revealed that Fenbendazole and Rafoxanide remain at the binding pocket of the LuxR protein most of the time. During the whole simulation period, backbone of all seven complexes displayed small fluctuation in the RMSD and Rg. The free energy landscape of LuxR–Rafoxanide and LuxR–Fenbendazole complexes showed promising stability compared to the rest of the complexes. Both the complexes showed excellent MMPBSA binding free energy: −92.882 ± 15.636 kJ/mol (LuxR–Rafoxanide) and −74.873 ± 9.233 kJ/mol (LuxR–Fenbendazole). The reactivity of the ligands was ascertained from their DFT‐based various descriptors.

The interactive walkway provides fit-for-purpose fall-risk biomarkers in the elderly: Comparison of zolpidem and suvorexant.

Dynamic balance assessments such as walking adaptability may yield a more realistic prediction of drug-induced falls compared with postural stability measurements, as falls often result from limited gait adjustments when walking. The Interactive Walkway (IWW) measures walking adaptability but sensitivity to medication effects is unknown. If proven sensitive and specific, IWW could serve as a biomarker for targeted fall-risk assessments in early clinical drug development. In this three-way crossover study, 18 healthy elderly (age: 65-80 years) subjects received 5 mg zolpidem, 10 mg suvorexant, or placebo in the morning. Assessments were performed pre-dose and approximately hourly until 9 h post-dose. IWW assessments included an 8-meter walking test, goal-directed stepping, obstacle-avoidance, and tandem-walking. Other pharmacodynamic measurements were the Timed-Up-and-Go (TUG) test at a comfortable and fast pace, adaptive tracking, and body sway. A decline in performance was observed for zolpidem compared with placebo for 3 h post-dose in IWW walking adaptability outcome measures, TUG, adaptive tracking, and body sway. For the IWW tasks, a decrease in walking speed (among others) was observed. IWW parameters were not affected by suvorexant compared with placebo at any timepoint. However, an increase of 9.8% (95%CI: 1.8%, 18.5%) in body sway was observed for suvorexant compared with placebo up to 3 h post-dose. The IWW successfully quantified drug effects of two hypnotic drugs and distinguished between zolpidem and suvorexant regarding their effects on walking. As a biomarker, the IWW demonstrated sensitivity in assessing dynamic balance and potential fall risk in early phase clinical drug development.

Assessing Patient Risk, Benefit, and Outcomes in Drug Development: Insights From Afatinib Clinical Trials Across Diverse Cancer Indications

PurposeIn 2013, afatinib was approved for non–small-cell lung cancer with subsequent indication expansion. We investigated published afatinib clinical trials to assess risk and benefit profiles for the drug in its approved indication of non–small-cell lung cancer as well as in off-label uses. Previous literature demonstrates excessive patient burden and limited benefit as afatinib has spread into more indications. A trial analysis is needed to establish efficacy and risk. MethodsIn this investigation, we screened literature databases and clinical trial registries for trials of afatinib as monotherapy or in combination interventions for cancer treatment. We extracted participant demographics, adverse event characteristics, as well as clinical and surrogate endpoints for each trial. Studies were deemed positive, negative, or indeterminate based on their achieving of primary endpoints as well as their safety. ResultsOur search yielded 2444 articles; we excluded 2352 articles for a final inclusion of 92 trials of 8859 patients. Our sample had 49 (53%) positive trials, 27 (29%) negative trials, and 16 (17%) indeterminate trials. The most common off-label indications for afatinib were breast cancer and squamous cell carcinoma of head and neck. The median OS for all trials was 8.4 months, median PFS 3.4 months, and the total ORR was 29.6%. Our study found that trials performed in disease states beyond the initial indications were largely negative with little patient benefit. The adverse events within our trial sample appear to be in line with expectations for toxicity. ImplicationsThese results are consistent with other studies that present similar findings, such as in Carlisle et al which indicate limited efficacy in nonapproved indications. Future trials should keep this potential evidence and patient burden in mind before initiation of those trials. This study contributes to the understanding of afatinib's risk-benefit profile across many clinical applications.

Assessing patient risk, benefit, and outcomes in drug development: A decade of ramucirumab clinical trials.

This study aims to evaluate published clinical trials of ramucirumab to assess the risk/benefit profile and burden over time for patients. The burden of oncologic drug development on patients paired with increasing clinical trial failure rates emphasizes the need for reform of drug development. Identifying and addressing patterns of excess burden can guide policy, ensure evidence-based protections for trial participants, and improve medical decision-making. On May 25, 2023 a literature search was performed on Pubmed/MEDLINE, Embase, Cochrane CENTRAL, and ClinicalTrials.gov for clinical trials using ramucirumab as monotherapy or in combination with other interventions for cancer treatment. Authors screened titles and abstracts for potential inclusion in a masked, duplicate fashion. Following data screening, data was extracted in a masked, duplicate fashion. Trials were classified as positive when meeting their primary endpoint and safety, negative or indeterminate. Ramucirumab was initially approved for gastric cancer but has since been tested in 20 cancers outside of its FDA approved indications. In our analysis of ramucirumab trials, there were a total of 10,936 participants and 10,303 adverse events reported. Gains in overall survival and progression-free survival for patients were 1.5 and 1.2 months, respectively. FDA-approved indications have reported more positive outcomes in comparison to off-label indications. We found that FDA-approved indications for ramucirumab had better efficacy outcomes than non-approved indications. However, a concerning number of adverse events were observed across all trials assessed. Participants in ramucirumab randomized controlled trials saw meager gains in overall survival when evaluated against a comparison group. Clinicians should carefully weigh the risks associated with ramucirumab therapy given its toxicity burden and poor survival gains.

Assessing patient risk, benefit and outcomes in drug development: an observational study of regorafenib clinical trials

ObjectiveOur objective of this study was to analyse all oncological clinical trials using regorafenib to create a complete risk/benefit profile for the drug.BackgroundCreating a novel chemotherapy is costly both in...

Assessing Patient Risk, Benefit, and Outcomes in Drug Development: A Decade of Lenvatinib Clinical Trials: A Systematic Review.

Chemotherapy agents are typically initially tested in their most promising indications; however, following initial US FDA approval, new clinical trials are often initiated in less promising indications where patients experience a worse burden-benefit ratio. The current literature on the burden-benefit profile of lenvatinib in non-FDA-approved indications is lacking. This study aimed to evaluate published clinical trials of lenvatinib in order to determine the burden-benefit profile for patients over time. On 25 May 2023, we searched the Pubmed/MEDLINE, Embase, Cochrane CENTRAL, and ClinicalTrials.gov databases for clinical trials of lenvatinib used to treat solid cancers. Eligible articles were clinical trials, containing adult participants, published in English, and involving solid tumors. Screening and data collection took place in a masked, duplicate fashion. For each eligible study, we collected adverse event data, trial characteristics, progression-free survival (PFS), overall survival (OS), and objective response rate (ORR). Trials were classified as positive when meeting their primary endpoint and safety, negative (not meeting either criteria), or indeterminate (lacking prespecified primary endpoint). Expansion of clinical trial testing beyond lenvatinib's initial FDA indication demonstrated a consistent rise in cumulative adverse events, along with a decline in drug efficacy. Lenvatinib was tested in 16 cancer indications, receiving FDA approval in 4. A total of 5390 Grade 3-5 adverse events were experienced across 6225 clinical trial participants. Expanded indication testing further demonstrated widely variable ORR (11-69%), OS (6.2-32 months), and PFS (3.6-15.7 months) across all indications. After initial FDA approval, clinical trial results in expanded indications were less likely to meet their primary endpoints, particularly among non-randomized clinical trials. Our paper evaluated the effectiveness of lenvatinib for its FDA-approved indications; however, expansion of clinical trials into novel indications was characterized by diminished efficacy, while patients experienced a high burden of adverse events consistent with lenvatinib's established safety profile. Furthermore, clinical trials testing in novel indications was marked by repeated phase I and II clinical trials along with a failure to progress to phase III clinical trials. Future clinical trials using lenvatinib as an intervention should carefully evaluate the potential benefits and burden patients may experience.

Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis.

Gallbladder (GB) disease is classified into two broad categories: GB wall-thickening and protuberant lesions, which include various lesions, such as adenomyomatosis, cholecystitis, GB polyps, and GB carcinoma. This review summarizes recent advances in the differential diagnosis of GB lesions, focusing primarily on endoscopic ultrasound (EUS) and related technologies. Fundamental B-mode EUS and contrast-enhanced harmonic EUS (CH-EUS) have been reported to be useful for the diagnosis of GB diseases because they can evaluate the thickening of the GB wall and protuberant lesions in detail. We also outline the current status of EUS-guided fine-needle aspiration (EUS-FNA) for GB lesions, as there have been scattered reports on EUS-FNA in recent years. Furthermore, artificial intelligence (AI) technologies, ranging from machine learning to deep learning, have become popular in healthcare for disease diagnosis, drug discovery, drug development, and patient risk identification. In this review, we outline the current status of AI in the diagnosis of GB.

WANTED DEAD OR ALIVE: New Thinking to Incentivize Drug Development.

Despite unprecedented advancements in our understanding of disease process and targeting opportunities, our continued ability to discover and develop new medicines is fundamentally in doubt. These challenges reflect a growing recognition of the escalating risks of drug development (in terms of dollars and time) by established pharmaceutical companies and investors. Such hurdles are surmountable and require new thinking. This opinion piece conveys an overview of the challenge and an example of how we might overcome these challenges with constructive and forward-looking incentives for drug discovery.

Oncogenic β-catenin-driven liver cancer is susceptible to methotrexate-mediated disruption of nucleotide synthesis.

Liver cancer is largely resistant to chemotherapy. This study aimed to identify the effective chemotherapeutics for β-catenin-activated liver cancer which is caused by gain-of-function mutation of catenin beta 1 ( CTNNB1 ), the most frequently altered proto-oncogene in hepatic neoplasms. Constitutive β-catenin-activated mouse embryonic fibroblasts (MEFs) were established by deleting exon 3 ( β-catenin Δ(ex3)/+ ), the most common mutation site in CTNNB1 gene. A screening of 12 widely used chemotherapy drugs was conducted for the ones that selectively inhibited β-catenin Δ(ex3)/+ but not for wild-type MEFs. Untargeted metabolomics was carried out to examine the alterations of metabolites in nucleotide synthesis. The efficacy and selectivity of methotrexate (MTX) on β-catenin-activated human liver cancer cells were determined in vitro . Immuno-deficient nude mice subcutaneously inoculated with β-catenin wild-type or mutant liver cancer cells and hepatitis B virus ( HBV ); β-catenin lox(ex3)/+ mice were used, respectively, to evaluate the efficacy of MTX in the treatment of β-catenin mutant liver cancer. MTX was identified and validated as a preferential agent against the proliferation and tumor formation of β-catenin-activated cells. Boosted nucleotide synthesis was the major metabolic aberration in β-catenin-active cells, and this alteration was also the target of MTX. Moreover, MTX abrogated hepatocarcinogenesis of HBV ; β-catenin lox(ex3)/+ mice, which stimulated concurrent Ctnnb1- activated mutation and HBV infection in liver cancer. MTX is a promising chemotherapeutic agent for β-catenin hyperactive liver cancer. Since repurposing MTX has the advantages of lower risk, shorter timelines, and less investment in drug discovery and development, a clinical trial is warranted to test its efficacy in the treatment of β-catenin mutant liver cancer.

Drug Repurposing in Oncology: A Systematic Review of Randomized Controlled Clinical Trials.

Quality pharmacological treatment can improve survival in many types of cancer. Drug repurposing offers advantages in comparison with traditional drug development procedures, reducing time and risk. This systematic review identified the most recent randomized controlled clinical trials that focus on drug repurposing in oncology. We found that only a few clinical trials were placebo-controlled or standard-of-care-alone-controlled. Metformin has been studied for potential use in various types of cancer, including prostate, lung, and pancreatic cancer. Other studies assessed the possible use of the antiparasitic agent mebendazole in colorectal cancer and of propranolol in multiple myeloma or, when combined with etodolac, in breast cancer. We were able to identify trials that study the potential use of known antineoplastics in other non-oncological conditions, such as imatinib for severe coronavirus disease in 2019 or a study protocol aiming to assess the possible repurposing of leuprolide for Alzheimer's disease. Major limitations of these clinical trials were the small sample size, the high clinical heterogeneity of the participants regarding the stage of the neoplastic disease, and the lack of accounting for multimorbidity and other baseline clinical characteristics. Drug repurposing possibilities in oncology must be carefully examined with well-designed trials, considering factors that could influence prognosis.

Improved Computational Drug-Repositioning by Self-Paced Non-Negative Matrix Tri-Factorization.

Drug repositioning (DR) is a strategy to find new targets for existing drugs, which plays an important role in reducing the costs, time, and risk of traditional drug development. Recently, the matrix factorization approach has been widely used in the field of DR prediction. Nevertheless, there are still two challenges: 1) Learning ability deficiencies, the model cannot accurately predict more potential associations. 2) Easy to fall into a bad local optimal solution, the model tends to get a suboptimal result. In this study, we propose a self-paced non-negative matrix tri-factorization (SPLNMTF) model, which integrates three types of different biological data from patients, genes, and drugs into a heterogeneous network through non-negative matrix tri-factorization, thereby learning more information to improve the learning ability of the model. In the meantime, the SPLNMTF model sequentially includes samples into training from easy (high-quality) to complex (low-quality) in the soft weighting way, which effectively alleviates falling into a bad local optimal solution to improve the prediction performance of the model. The experimental results on two real datasets of ovarian cancer and acute myeloid leukemia (AML) show that SPLNMTF outperforms the other eight state-of-the-art models and gets better prediction performance in drug repositioning. The data and source code are available at: https://github.com/qi0906/SPLNMTF.

Toxicogenomics: opportunities and challenges of application in drug development, toxicity prediction and human health risk assessment

Toxicogenomics: opportunities and challenges of application in drug development, toxicity prediction and human health risk assessment

Novel antimicrobial agents targeting the Streptococcus mutans biofilms discovery through computer technology.

Dental caries is one of the most prevalent and costly biofilm-associated infectious diseases worldwide. Streptococcus mutans (S. mutans) is well recognized as the major causative factor of dental caries due to its acidogenicity, aciduricity and extracellular polymeric substances (EPSs) synthesis ability. The EPSs have been considered as a virulent factor of cariogenic biofilm, which enhance biofilms resistance to antimicrobial agents and virulence compared with planktonic bacterial cells. The traditional anti-caries therapies, such as chlorhexidine and antibiotics are characterized by side-effects and drug resistance. With the development of computer technology, several novel approaches are being used to synthesize or discover antimicrobial agents. In this mini review, we summarized the novel antimicrobial agents targeting the S. mutans biofilms discovery through computer technology. Drug repurposing of small molecules expands the original medical indications and lowers drug development costs and risks. The computer-aided drug design (CADD) has been used for identifying compounds with optimal interactions with the target via silico screening and computational methods. The synthetic antimicrobial peptides (AMPs) based on the rational design, computational design or high-throughput screening have shown increased selectivity for both single- and multi-species biofilms. These methods provide potential therapeutic agents to promote targeted control of the oral microbial biofilms in the near future.

Imaging Technologies for Cerebral Pharmacokinetic Studies: Progress and Perspectives.

Pharmacokinetic assessment of drug disposition processes in vivo is critical in predicting pharmacodynamics and toxicology to reduce the risk of inappropriate drug development. The blood–brain barrier (BBB), a special physiological structure in brain tissue, hinders the entry of targeted drugs into the central nervous system (CNS), making the drug concentrations in target tissue correlate poorly with the blood drug concentrations. Additionally, once non-CNS drugs act directly on the fragile and important brain tissue, they may produce extra-therapeutic effects that may impair CNS function. Thus, an intracerebral pharmacokinetic study was developed to reflect the disposition and course of action of drugs following intracerebral absorption. Through an increasing understanding of the fine structure in the brain and the rapid development of analytical techniques, cerebral pharmacokinetic techniques have developed into non-invasive imaging techniques. Through non-invasive imaging techniques, molecules can be tracked and visualized in the entire BBB, visualizing how they enter the BBB, allowing quantitative tools to be combined with the imaging system to derive reliable pharmacokinetic profiles. The advent of imaging-based pharmacokinetic techniques in the brain has made the field of intracerebral pharmacokinetics more complete and reliable, paving the way for elucidating the dynamics of drug action in the brain and predicting its course. The paper reviews the development and application of imaging technologies for cerebral pharmacokinetic study, represented by optical imaging, radiographic autoradiography, radionuclide imaging and mass spectrometry imaging, and objectively evaluates the advantages and limitations of these methods for predicting the pharmacodynamic and toxic effects of drugs in brain tissues.

Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors.

Drug-metabolizing enzyme (DME)-mediated pharmacokinetic resistance of some clinically approved anticancer agents is one of the main reasons for cancer treatment failure. In particular, some commonly used anticancer medicines, including docetaxel, tamoxifen, imatinib, cisplatin, and paclitaxel, are inactivated by CYP1B1. Currently, no approved drugs are available to treat this CYP1B1-mediated inactivation, making the pharmaceutical industries strive to discover new anticancer agents. Because of the extreme complexity and high risk in drug discovery and development, it is worthwhile to come up with a drug repurposing strategy that may solve the resistance problem of existing chemotherapeutics. Therefore, in the current study, a drug repurposing strategy was implemented to find the possible CYP1B1 inhibitors using machine learning (ML) and structure-based virtual screening (SB-VS) approaches. Initially, three different ML models were developed such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN); subsequently, the best-selected ML model was employed for virtual screening of the selleckchem database to identify potential CYP1B1 inhibitors. The inhibition potency of the obtained hits was judged by analyzing the crucial active site amino acid interactions against CYP1B1. After a thorough assessment of docking scores, binding affinities, as well as binding modes, four compounds were selected and further subjected to in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, and ticagrelor showed promising inhibitory activity toward CYP1B1 in the IC50 range of 0.07–3.00 μM. These new chemical scaffolds can be explored as adjuvant therapies to address CYP1B1-mediated drug-resistance problems.

Balancing Risk and Efficiency in Drug Development for Rare and Challenging Tumors: A New Paradigm for Glioma.

The process of developing cancer therapies is well established and has enabled the incorporation of many new drugs and classes of agents into the standard of care for common cancers. Clinical drug development is fundamentally different for rare and difficult-to-treat solid tumors, such as glioma or pancreatic cancer. The failure to develop effective new agents for the latter diseases has discouraged the development of therapeutics for these cancers. Using glioma as an example, we describe a process toward obtaining more reliable early-stage signals of drug activity and a process toward translating those signals into clinical benefits with more efficient late-stage development. If linked together, these processes should increase the likelihood of benefit in late-stage settings at a lower cost and encourage more drug development for patients with rare and difficult-to-treat cancers.

Risk assessment for nitrosated pharmaceuticals: A future perspective in drug development.

Since June 2018, thousands of drug products from around the world had to be recalled due to the unexpected presence of nitrosamines (NAs). Starting with the pharmaceutical group of sartans, antidiabetic drugs, antihistamines, and antibiotics also became the subject of investigation. The occurrence of NAs has shown that pharmaceutical companies and regulatory agencies did not focus on these substances in the past during drug development. In this study, we incorporated a nitrosation assay procedure into high-resolution supercritical fluid chromatography (SFC)-mass spectrometry screening to test the potential of direct nitrosation of active pharmaceutical ingredients (APIs). The forced degradation study was performed with a four-fold molar excess of sodium nitrite, relative to the drug substance, at pH 3-4 for 4 h at 37°C. Chromatographic separation was performed on a porous graphitic carbon column by SFC. The mass analysis then focused on direct N-nitrosation or N-nitroso compounds (NOCs) formed after dealkylation. Substances (n = 67) from various pharmaceutical classes were evaluated and 49.3% of them formed NOCs, of which 21.2% have not yet been reported in the literature. In addition, for two APIs, which are known to form an unidentified NOC, the structure could be identified. A few substances also showed multiple NOCs and even N,N'-dinitroso-species. As NAs are carcinogens, they have to be eliminated or at least limited to prevent cancer in patients, who rely on these drugs. This study contributes a procedure that can be implemented in preapproval drug development and postapproval risk assessment to prevent unexpected findings in the future.

Development and Evaluation of an In Silico Dermal Absorption Model Relevant for Children.

The higher skin surface area to body weight ratio in children and the prematurity of skin in neonates may lead to higher chemical exposure as compared to adults. The objectives of this study were: (i) to provide a comprehensive review of the age-dependent anatomical and physiological changes in pediatric skin, and (ii) to construct and evaluate an age-dependent pediatric dermal absorption model. A comprehensive review was conducted to gather data quantifying the differences in the anatomy and physiology of child and adult skin. Maturation functions were developed for model parameters that were found to be age-dependent. A pediatric dermal absorption model was constructed by updating a MoBi implementation of the Dancik et al. 2013 skin permeation model with these maturation functions. Using a workflow for adult-to-child model extrapolation, the predictive performance of the model was evaluated by comparing its predicted rates of flux of diamorphine, phenobarbital and buprenorphine against experimental observations using neonatal skin. For diamorphine and phenobarbital, the model provided reasonable predictions. The ratios of predicted:observed flux in neonates for diamorphine ranged from 0.55 to 1.40. For phenobarbital, the ratios ranged from 0.93 to 1.26. For buprenorphine, the model showed acceptable predictive performance. Overall, the physiologically based pediatric dermal absorption model demonstrated satisfactory prediction accuracy. The prediction of dermal absorption in neonates using a model-based approach will be useful for both drug development and human health risk assessment.

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.

Drug Repurposing Strategy (DRS): Emerging Approach to Identify Potential Therapeutics for Treatment of Novel Coronavirus Infection.

Drug repurposing is also termed as drug repositioning or therapeutic switching. This method is applied to identify the novel therapeutic agents from the existing FDA approved clinically used drug molecules. It is considered as an efficient approach to develop drug candidates with new pharmacological activities or therapeutic properties. As the drug discovery is a costly, time-consuming, laborious, and highly risk process, the novel approach of drug repositioning is employed to increases the success rate of drug development. This strategy is more advantageous over traditional drug discovery process in terms of reducing duration of drug development, low-cost, highly efficient and minimum risk of failure. In addition to this, World health organization declared Coronavirus disease (COVID-19) as pandemic globally on February 11, 2020. Currently, there is an urgent need to develop suitable therapeutic agents for the prevention of the outbreak of COVID-19. So, various investigations were carried out to design novel drug molecules by utilizing different approaches of drug repurposing to identify drug substances for treatment of COVID-19, which can act as significant inhibitors against viral proteins. It has been reported that COVID-19 can infect human respiratory system by entering into the alveoli of lung via respiratory tract. So, the infection occurs due to specific interaction or binding of spike protein with angiotensin converting enzyme-2 (ACE-2) receptor. Hence, drug repurposing strategy is utilized to identify suitable drugs by virtual screening of drug libraries. This approach helps to determine the binding interaction of drug candidates with target protein of coronavirus by using computational tools such as molecular similarity and homology modeling etc. For predicting the drug-receptor interactions and binding affinity, molecular docking study and binding free energy calculations are also performed. The methodologies involved in drug repurposing can be categorized into three groups such as drug-oriented, target-oriented and disease or therapy-oriented depending on the information available related to quality and quantity of the physico-chemical, biological, pharmacological, toxicological and pharmacokinetic property of drug molecules. This review focuses on drug repurposing strategy applied for existing drugs including Remdesivir, Favipiravir, Ribavirin, Baraticinib, Tocilizumab, Chloroquine, Hydroxychloroquine, Prulifloxacin, Carfilzomib, Bictegravir, Nelfinavir, Tegobuvir and Glucocorticoids etc to determine their effectiveness toward the treatment of COVID-19.