Drug Repurposing on G Protein-Coupled Receptors Using a Computational Profiling Approach.
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ABSTRACT:
SUBMITTER: de Felice A
PROVIDER: S-EPMC8138314 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Motivation:Identification of novel therapeutic effects for existing US Food and Drug Administration (FDA)-approved drugs, drug repurposing, is an approach aimed to dramatically shorten the drug discovery process, which is costly, slow and risky. Several computational approaches use transcriptional data to find potential repurposing candidates. The main hypothesis of such approaches is that if gene expression signature of a particular drug is opposite to the gene expression signature of a disease, that drug may have a potential therapeutic effect on the disease. However, this may not be optimal since it fails to consider the different roles of genes and their dependencies at the system level. Results:We propose a systems biology approach to discover novel therapeutic roles for established drugs that addresses some of the issues in the current approaches. To do so, we use publicly available drug and disease data to build a drug-disease network by considering all interactions between drug targets and disease-related genes in the context of all known signaling pathways. This network is integrated with gene-expression measurements to identify drugs with new desired therapeutic effects based on a system-level analysis method. We compare the proposed approach with the drug repurposing approach proposed by Sirota et al. on four human diseases: idiopathic pulmonary fibrosis, non-small cell lung cancer, prostate cancer and breast cancer. We evaluate the proposed approach based on its ability to re-discover drugs that are already FDA-approved for a given disease. Availability and implementation:The R package DrugDiseaseNet is under review for publication in Bioconductor and is available at https://github.com/azampvd/DrugDiseaseNet. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6084573 | biostudies-literature
Project description:Background: In the current SARS-CoV-2 outbreak, drug repositioning emerges as a promising approach to develop efficient therapeutics in comparison to de novo drug development. The present investigation screened 130 US FDA-approved drugs including hypertension, cardiovascular diseases, respiratory tract infections (RTI), antibiotics and antiviral drugs for their inhibitory potential against SARS-CoV-2. Materials & methods: The molecular drug targets against SARS-CoV-2 proteins were determined by the iGEMDOCK computational docking tool. The protein homology models were generated through SWISS Model workspace. The pharmacokinetics of all the ligands was determined by ADMET analysis. Results: The study identified 15 potent drugs exhibiting significant inhibitory potential against SARS-CoV-2. Conclusion: Our investigation has identified possible repurposed drug candidates to improve the current modus operandi of the treatment given to COVID-19 patients.
| S-EPMC7923689 | biostudies-literature
Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has generated a critical need for treatments to reduce morbidity and mortality associated with this disease. However, traditional drug development takes many years, which is not practical solution given the current pandemic. Therefore, a viable option is to repurpose existing drugs. The structural data of several proteins vital for the virus became available shortly after the start of the pandemic. In this review, we discuss the importance of these targets and their available potential inhibitors predicted by the computational approaches. Among the hits identified by computational approaches, 35 candidates were suggested for further evaluation, among which ten drugs are in clinical trials (Phase III and IV) for treating Coronavirus 2019 (COVID-19).
| S-EPMC8830191 | biostudies-literature
Project description:BackgroundDespite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated.ResultsWe developed a computational method termed "drug-protein interaction-based repurposing" (DPIR) that is potentially applicable to diseases with very few approved drugs. The method, based on genome-wide drug-protein interaction information and Bayesian statistics, first identifies drug-protein interactions associated with a desired therapeutic effect. Then, it uses key drug-protein interactions to score other drugs for their potential to have the same therapeutic effect.ConclusionsDetailed cross-validation studies using United States Food and Drug Administration-approved drugs for hypertension, human immunodeficiency virus, and malaria indicated that DPIR provides robust predictions. It achieves high levels of enrichment of drugs approved for a disease even with models developed based on a single drug known to treat the disease. Analysis of our model predictions also indicated that the method is potentially useful for understanding molecular mechanisms of drug action and for identifying protein targets that may potentiate the desired therapeutic effects of other drugs (combination therapies).
| S-EPMC4079911 | biostudies-literature
Project description:Developing drugs to treat the toxic effects of lethal toxin (LT) and edema toxin (ET) produced by B. anthracis is of global interest. We utilized a computational approach to score 474 drugs/compounds for their ability to reverse the toxic effects of anthrax toxins. For each toxin or drug/compound, we constructed an activity network by using its differentially expressed genes, molecular targets, and protein interactions. Gene expression profiles of drugs were obtained from the Connectivity Map and those of anthrax toxins in human alveolar macrophages were obtained from the Gene Expression Omnibus. Drug rankings were based on the ability of a drug/compound's mode of action in the form of a signaling network to reverse the effects of anthrax toxins; literature reports were used to verify the top 10 and bottom 10 drugs/compounds identified. Simvastatin and bepridil with reported in vitro potency for protecting cells from LT and ET toxicities were computationally ranked fourth and eighth. The other top 10 drugs were fenofibrate, dihydroergotamine, cotinine, amantadine, mephenytoin, sotalol, ifosfamide, and mefloquine; literature mining revealed their potential protective effects from LT and ET toxicities. These drugs are worthy of investigation for their therapeutic benefits and might be used in combination with antibiotics for treating B. anthracis infection.
| S-EPMC5371854 | biostudies-literature
Project description:MOTIVATION:Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. RESULTS:We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. AVAILABILITY AND IMPLEMENTATION:The R scripts are available on demand from the authors. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6761937 | biostudies-literature
Project description:Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects nearly eight million people worldwide. There are currently only limited treatment options, which cause several side effects and have drug resistance. Thus, there is a great need for a novel, improved Chagas treatment. Bifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS) has emerged as a promising pharmacological target. Moreover, some human dihydrofolate reductase (HsDHFR) inhibitors such as trimetrexate also inhibit T. cruzi DHFR-TS (TcDHFR-TS). These compounds serve as a starting point and a reference in a screening campaign to search for new TcDHFR-TS inhibitors. In this paper, a novel virtual screening approach was developed that combines classical docking with protein-ligand interaction profiling to identify drug repositioning opportunities against T. cruzi infection. In this approach, some food and drug administration (FDA)-approved drugs that were predicted to bind with high affinity to TcDHFR-TS and whose predicted molecular interactions are conserved among known inhibitors were selected. Overall, ten putative TcDHFR-TS inhibitors were identified. These exhibited a similar interaction profile and a higher computed binding affinity, compared to trimetrexate. Nilotinib, glipizide, glyburide and gliquidone were tested on T. cruzi epimastigotes and showed growth inhibitory activity in the micromolar range. Therefore, these compounds could lead to the development of new treatment options for Chagas disease.
| S-EPMC7348847 | biostudies-literature
Project description:Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.
| S-EPMC8791995 | biostudies-literature
Project description:Monkeypox viral infection is an emerging threat and a major concern for the human population. The lack of drug molecules to treat this disease may worsen the problem. Identifying potential drug targets can significantly improve the process of developing potent drug molecules for treating monkeypox. The proteins responsible for viral replication are attractive drug targets. Identifying potential inhibitors from known drug molecules that target these proteins can be key to finding a cure for monkeypox. In this work, two viral proteins, DNA-dependent RNA polymerase (DdRp) and viral core cysteine proteinase, were considered as potential drug targets. Sixteen antibiotic drugs from the tetracycline class were screened against both viral proteins through high-throughput virtual screening. These tetracycline class of antibiotic drugs have the ability to inhibit bacterial protein synthesis, which makes these antibiotics drugs a prominent candidate for drug repurposing. Based on the screening result obtained against DdRp, top two compounds, namely Tigecycline and Eravacycline with docking scores of - 8.88 and - 7.87 kcal/mol, respectively, were selected for further analysis. Omadacycline and minocycline, with docking scores of - 10.60 and - 7.51 kcal/mol, are the top two compounds obtained after screening proteinase with the drug library. These compounds, along with reference compounds GTP for DdRp and tecovirimat for proteinase, were used to form protein-ligand complexes, followed by their evaluation through a 300 ns molecular dynamic simulation. The MM/GBSA binding free energy calculation and principal components analysis of these selected complexes were also conducted for understanding the dynamic stability and binding affinity of these compounds with respective target proteins. Overall, this study demonstrates the repurposing of tetracycline-derived drugs as a therapeutic solution for monkeypox viral infection.
| S-EPMC10477205 | biostudies-literature
Project description:PurposeRecently, the world has been dealing with a new type of coronavirus called COVID-19 that in terms of symptoms is similar to the SARS coronavirus. Unfortunately, researchers could not find a registered therapy to treat the infection related to the virus yet. Regarding the fact that drug repurposing is a good strategy for epidemic viral infection, we applied the drug repurposing strategy using virtual screening to identify therapeutic options for COVID-19. For this purpose, five proteins of COVID-19 (3-chymotrypsin-like protease (3CLpro), Papain-Like protease (PLpro), cleavage site, HR1 and RBD in Spike protein) were selected as target proteins for drug repositioning.MethodsFirst, five proteins of COVID-19 were built by homology modeling. Then FDA-approved drugs (2471 drugs) were screened against cleavage site and RBD in Spike protein via virtual screening. One hundred and twenty-eight FDA-approved drugs with the most favorable free-binding energy were attached to the cleavage site and RBD in Spike protein. Of these 128 drugs, 18 drugs have either been used currently as antiviral or have been reported to possess antiviral effects. Virtual screening was then performed for the 18 selected drugs with ACE2, 3CLpro and PLpro and HR1 and TMPRSS2.ResultsAccording to the results, glecaprevir, paritaprevir, simeprevir, ledipasvir, glycyrrhizic acid, TMC-310911, and hesperidin showed highly favorably free binding energies with all tested target proteins.ConclusionThe above-mentioned drugs can be regarded as candidates to treat COVID-19 infections, but further study on the efficiency of these drugs is also necessary.
| S-EPMC7261216 | biostudies-literature