Comput Biol Med. 2023 Jul 18;164:107261. doi: 10.1016/j.compbiomed.2023.107261. Online ahead of print.

ABSTRACT

Experimental drug development is costly, complex, and time-consuming, and the number of drugs that have been put into application treatment is small. The identification of drug-disease correlations can provide important information for drug discovery and drug repurposing. Computational drug repurposing is an important and effective method that can be used to determine novel treatments for diseases. In recent years, an increasing number of large databases have been utilized for biological data research, particularly in the fields of drugs and diseases. Consequently, researchers have begun to explore the application of deep neural networks in biological data development. One particularly promising method for unsupervised learning is the deep generative model, with the variational autoencoder (VAE) being among the mainstream models. Here, we propose a drug indication prediction algorithm called DIDVAE (predicting new drug indications based on double variational autoencoders), which generates new data by learning the latent variable distribution of known data to achieve the goal of predicting drug-disease associations. In the experiment, we compared the DIDVAE algorithm with the BBNR, DrugNet, MBiRW and DRRS algorithms on a unified dataset. The comprehensive experimental results show that, compared with these prediction algorithms, the DIDVAE algorithm provides an overall improved prediction. In addition, further analysis and verification of the predicted unknown drug-disease association also proved the practicality of the method.

PMID:37487382 | DOI:10.1016/j.compbiomed.2023.107261

Comput Biol Chem. 2023 Jul 21;106:107934. doi: 10.1016/j.compbiolchem.2023.107934. Online ahead of print.

ABSTRACT

Regeneration is a homeostatic process that involves the restoration of cells and body parts. Most of the molecular mechanisms and signalling pathways involved in wound healing, such as proliferation, have also been associated with cancer cell growth, suggesting that cancer is an over/unhealed wound. In this study, we examined differentially expressed genes in spinal cord samples from regenerative organisms (axolotl and zebrafish) and nonregenerative organisms (mouse and rat) compared to intact control spinal cord samples using publicly available transcriptomics data and bioinformatics analyses. Based on these gene signatures, we investigated 3 small compounds, namely cucurbitacin I, BMS-754807, and PHA-793887 as potential candidates for the treatment of cancer. The predicted target genes of the repositioned compounds were mainly enriched with the greatest number of genes in cancer pathways. The molecular docking results on the binding affinity between the repositioned compounds and their target genes are also reported. The repositioned 3 small compounds showed anticancer effect both in 2D and 3D cell cultures using the prostate cancer cell line as a model. We propose cucurbitacin I, BMS-754807, and PHA-793887 as potential anticancer drug candidates. Future studies on the mechanisms associated with the revealed gene signatures and anticancer effects of these three small compunds would allow scientists to develop therapeutic approaches to combat cancer. This research contributes to the evaluation of mechanisms and gene signatures that either limit or cause cancer, and to the development of new cancer therapies by establishing a link between regeneration and carcinogenesis.

PMID:37487250 | DOI:10.1016/j.compbiolchem.2023.107934

Adv Exp Med Biol. 2023;1424:201-211. doi: 10.1007/978-3-031-31982-2_22.

ABSTRACT

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disease characterized by progressive loss of the upper and lower motor neurons. There are currently limited approved drugs for the disorder, and for this reason the strategy of repositioning already approved therapeutics could exhibit a successful outcome. Herein, we used CMAP and L1000CDS2 databases which include gene expression profiles datasets (genomic signatures) to identify potent compounds and classes of compounds which reverse disease's signature which could in turn reverse its phenotype. ALS signature was obtained by comparing gene expression of muscle biopsy specimens between diseased and healthy individuals. Statistical analysis was conducted to explore differentially transcripts in patients' samples. Then, the list of upregulated and downregulated genes was used to query both databases in order to determine molecules which downregulate the genes which are upregulated by ALS and vice versa. These compounds, based on their chemical structure along with known treatments, were clustered to reveal drugs with novel and potentially more effective mode of action with most of them predicted to affect pathways heavily involved in ALS. This evidence suggests that these compounds are strong candidates for moving to the next phase of the drug repurposing pipeline which is in vitro and in vivo experimental evaluation.

PMID:37486495 | DOI:10.1007/978-3-031-31982-2_22

Comput Struct Biotechnol J. 2023 Jul 7;21:3490-3502. doi: 10.1016/j.csbj.2023.07.007. eCollection 2023.

ABSTRACT

Renal inflammation and fibrosis are significantly correlated with the deterioration of kidney function and result in chronic kidney disease (CKD). However, current therapies only delay disease progression and have limited treatment effects. Hence, the development of innovative therapeutic approaches to mitigate the progression of CKD has become an attractive issue. To date, the incidence of CKD is still increasing, and the biomarkers of the pathophysiologic processes of CKD are not clear. Therefore, the identification of novel therapeutic targets associated with the progression of CKD is an attractive issue. It is a critical necessity to discover new therapeutics as nephroprotective strategies to stop CKD progression. In this research, we focus on targeting a prostaglandin E2 receptor (EP2) as a nephroprotective strategy for the development of additional anti-inflammatory or antifibrotic strategies for CKD. The in silico study identified that ritodrine, dofetilide, dobutamine, and citalopram are highly related to EP2 from the results of chemical database virtual screening. Furthermore, we found that the above four candidate drugs increased the activation of autophagy in human kidney cells, which also reduced the expression level of fibrosis and NLRP3 inflammasome activation. It is hoped that these findings of the four candidates with anti-NLRP3 inflammasome activation and antifibrotic effects will lead to the development of novel therapies for patients with CKD in the future.

PMID:37484490 | PMC:PMC10362296 | DOI:10.1016/j.csbj.2023.07.007

Comput Biol Med. 2023 Jul 17;164:107262. doi: 10.1016/j.compbiomed.2023.107262. Online ahead of print.

ABSTRACT

Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.

PMID:37481946 | DOI:10.1016/j.compbiomed.2023.107262

Trends Genet. 2023 Jul 21:S0168-9525(23)00134-8. doi: 10.1016/j.tig.2023.06.003. Online ahead of print.

ABSTRACT

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

PMID:37482451 | DOI:10.1016/j.tig.2023.06.003

Sci Rep. 2023 Jul 21;13(1):11783. doi: 10.1038/s41598-023-39071-z.

ABSTRACT

In this work, we present DrugSolver CavitomiX, a novel computational pipeline for drug repurposing and identifying ligands and inhibitors of target enzymes. The pipeline is based on cavity point clouds representing physico-chemical properties of the cavity induced solely by the protein. To test the pipeline's ability to identify inhibitors, we chose enzymes essential for SARS-CoV-2 replication as a test system. The active-site cavities of the viral enzymes main protease (Mpro) and papain-like protease (Plpro), as well as of the human transmembrane serine protease 2 (TMPRSS2), were selected as target cavities. Using active-site point-cloud comparisons, it was possible to identify two compounds-flufenamic acid and fusidic acid-which show strong inhibition of viral replication. The complexes from which fusidic acid and flufenamic acid were derived would not have been identified using classical sequence- and structure-based methods as they show very little structural (TM-score: 0.1 and 0.09, respectively) and very low sequence (~ 5%) identity to Mpro and TMPRSS2, respectively. Furthermore, a cavity-based off-target screening was performed using acetylcholinesterase (AChE) as an example. Using cavity comparisons, the human carboxylesterase was successfully identified, which is a described off-target for AChE inhibitors.

PMID:37479788 | DOI:10.1038/s41598-023-39071-z

Drug Res (Stuttg). 2023 Jul 21. doi: 10.1055/a-2095-0826. Online ahead of print.

ABSTRACT

Drug discovery and development is a time-consuming and costly procedure that necessitates a substantial effort. Drug repurposing has been suggested as a method for developing medicines that takes less time than developing brand new medications and will be less expensive. Also known as drug repositioning or re-profiling, this strategy has been in use from the time of serendipitous drug discoveries to the modern computer aided drug designing and use of computational chemistry. In the light of the COVID-19 pandemic too, drug repurposing emerged as a ray of hope in the dearth of available medicines. Data availability by electronic recording, libraries, and improvements in computational techniques offer a vital substrate for systemic evaluation of repurposing candidates. In the not-too-distant future, it could be possible to create a global research archive for us to access, thus accelerating the process of drug development and repurposing. This review aims to present the evolution, benefits and drawbacks including current approaches, key players and the legal and regulatory hurdles in the field of drug repurposing. The vast quantities of available data secured in multiple drug databases, assisting in drug repurposing is also discussed.

PMID:37478892 | DOI:10.1055/a-2095-0826

OMICS. 2023 Jul 21. doi: 10.1089/omi.2023.29094.correx. Online ahead of print.

NO ABSTRACT

PMID:37477879 | DOI:10.1089/omi.2023.29094.correx

Toxicol Appl Pharmacol. 2023 Jul 18:116630. doi: 10.1016/j.taap.2023.116630. Online ahead of print.

ABSTRACT

Gastric cancer (GC) is among the most-diagnosed and deadly malignancies worldwide. Deregulation in cellular bioenergetics is a hallmark of cancer. Based on the importance of metabolic reprogramming for the development and cancer progression, inhibitors of cell metabolism have been studied as potential candidates for chemotherapy in oncology. Mebendazole (MBZ), an antihelminthic approved by FDA, has shown antitumoral activity against cancer cell lines. However, its potential in the modulation of tumoral metabolism remains unclear. Results evidenced that the antitumoral and cytotoxic mechanism of MBZ in GC cells is related to the modulation of the mRNA expression of glycolic targets SLC2A1, HK1, GAPDH, and LDHA. Moreover, in silico analysis has shown that these genes are overexpressed in GC samples, and this increase in expression is related to decreased overall survival rates. Molecular docking revealed that MBZ modifies the protein structure of these targets, which may lead to changes in their protein function. In vitro studies also showed that MBZ induces alterations in glucose uptake, LDH's enzymatic activity, and ATP production. Furthermore, MBZ induced morphologic and intracellular alterations typical of the apoptotic cell death pathway. Thus, this data indicated that the cytotoxic mechanism of MBZ is related to an initial modulation of the tumoral metabolism in the GC cell line. Altogether, our results provide more evidence about the antitumoral mechanism of action of MBZ towards GC cells and reveal metabolic reprogramming as a potential area in the discovery of new pharmacological targets for GC chemotherapy.

PMID:37473966 | DOI:10.1016/j.taap.2023.116630

Psychiatry Res. 2023 Jul 11;326:115343. doi: 10.1016/j.psychres.2023.115343. Online ahead of print.

ABSTRACT

Anxiety disorders are a group of prevalent and heritable neuropsychiatric diseases. We previously conducted a genome-wide association study (GWAS) which identified genomic loci associated with anxiety; however, the biological consequences underlying the genetic associations are largely unknown. Integrating GWAS and functional genomic data may improve our understanding of the genetic effects on intermediate molecular phenotypes such as gene expression. This can provide an opportunity for the discovery of drug targets for anxiety via drug repurposing. We used the GWAS summary statistics to determine putative causal genes for anxiety using MAGMA and colocalization analyses. A transcriptome-wide association study was conducted to identify genes with differential genetically regulated levels of gene expression in human brain tissue. The genes were integrated with a large drug-gene expression database (Connectivity Map), discovering compounds that are predicted to "normalise" anxiety-associated expression changes. The study identified 64 putative causal genes associated with anxiety (35 genes upregulated; 29 genes downregulated). Drug mechanisms adrenergic receptor agonists, sigma receptor agonists, and glutamate receptor agonists gene targets were enriched in anxiety-associated genetic signal and exhibited an opposing effect on the anxiety-associated gene expression signature. The significance of the project demonstrated genetic links for novel drug candidates to potentially advance anxiety therapeutics.

PMID:37473490 | DOI:10.1016/j.psychres.2023.115343

Bioinformatics. 2023 Jul 20:btad442. doi: 10.1093/bioinformatics/btad442. Online ahead of print.

ABSTRACT

MOTIVATION: De novo drug development is a long and expensive process that poses significant challenges from the design to the pre-clinical testing, making the introduction into the market slow and difficult. This limitation paved the way to the development of drug repurposing, which consists in the re-usage of already approved drugs, developed for other therapeutic indications. Although several efforts have been carried out in the last decade in order to achieve clinically relevant drug repurposing predictions, the amount of repurposed drugs that have been employed in actual pharmacological therapies is still limited. On one hand, mechanistic approaches, including profile-based and network-based methods, exploit the wealth of data about drug sensitivity and perturbational profiles as well as disease transcriptomics profiles. On the other hand, chemocentric approaches, including structure-based methods, take into consideration the intrinsic structural properties of the drugs and their molecular targets. The poor integration between mechanistic and chemocentric approaches is one of the main limiting factors behind the poor translatability of drug repurposing predictions into the clinics.

RESULTS: In this work, we introduce DREAM, an R package aimed to integrate mechanistic and chemocentric approaches in a unified computational workflow. DREAM is devoted to the druggability evaluation of pathological conditions of interest, leveraging robust drug repurposing predictions. In addition, the user can derive optimised sets of drugs putatively suitable for combination therapy. In order to show the functionalities of the DREAM package, we report a case study on atopic dermatitis.

AVAILABILITY: DREAM is freely available at https://github.com/fhaive/dream. The docker image of DREAM is available at: https://hub.docker.com/r/fhaive/dream.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37471593 | DOI:10.1093/bioinformatics/btad442

Dev Med Child Neurol. 2023 Jul 19. doi: 10.1111/dmcn.15687. Online ahead of print.

ABSTRACT

Bachmann-Bupp syndrome (BABS) is a neurodevelopmental disorder characterized by developmental delay, hypotonia, and varying forms of non-congenital alopecia. The condition is caused by 3'-end mutations of the ornithine decarboxylase 1 (ODC1) gene, which produce carboxy (C)-terminally truncated variants of ODC, a pyridoxal 5'-phosphate-dependent enzyme. C-terminal truncation of ODC prevents its ubiquitin-independent proteasomal degradation and leads to cellular accumulation of ODC enzyme that remains catalytically active. ODC is the first rate-limiting enzyme that converts ornithine to putrescine in the polyamine pathway. Polyamines (putrescine, spermidine, spermine) are aliphatic molecules found in all forms of life and are important during embryogenesis, organogenesis, and tumorigenesis. BABS is an ultra-rare condition with few reported cases, but it serves as a convincing example for drug repurposing therapy. α-Difluoromethylornithine (DFMO, also known as eflornithine) is an ODC inhibitor with a strong safety profile in pediatric use for neuroblastoma and other cancers as well as West African sleeping sickness (trypanosomiasis). Patients with BABS have been treated with DFMO and have shown improvement in hair growth, muscle tone, and development.

PMID:37469105 | DOI:10.1111/dmcn.15687

Biochem Biophys Res Commun. 2023 Jul 12;675:113-121. doi: 10.1016/j.bbrc.2023.07.018. Online ahead of print.

ABSTRACT

The recent outbreak of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a severe threat to the global public health and economy, however, effective drugs to treat COVID-19 are still lacking. Here, we employ a deep learning-based drug repositioning strategy to systematically screen potential anti-SARS-CoV-2 drug candidates that target the cell entry mechanism of SARS-CoV-2 virus from 2635 FDA-approved drugs and 1062 active ingredients from Traditional Chinese Medicine herbs. In silico molecular docking analysis validates the interactions between the top compounds and host receptors or viral spike proteins. Using a SARS-CoV-2 pseudovirus system, we further identify several drug candidates including Fostamatinib, Linagliptin, Lysergol and Sophoridine that can effectively block the cell entry of SARS-CoV-2 variants into human lung cells even at a nanomolar scale. These efforts not only illuminate the feasibility of applying deep learning-based drug repositioning for antiviral agents by targeting a specified mechanism, but also provide a valuable resource of promising drug candidates or lead compounds to treat COVID-19.

PMID:37467664 | DOI:10.1016/j.bbrc.2023.07.018

Front Pharmacol. 2023 Jul 3;14:1242880. doi: 10.3389/fphar.2023.1242880. eCollection 2023.

NO ABSTRACT

PMID:37465521 | PMC:PMC10351978 | DOI:10.3389/fphar.2023.1242880

medRxiv. 2023 Jul 8:2023.07.07.23292388. doi: 10.1101/2023.07.07.23292388. Preprint.

ABSTRACT

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

PMID:37461512 | PMC:PMC10350158 | DOI:10.1101/2023.07.07.23292388

BMC Complement Med Ther. 2023 Jul 17;23(1):242. doi: 10.1186/s12906-023-04076-8.

ABSTRACT

Albendazole is the most common benzimidazole derivative used for trichinellosis treatment but has many drawbacks. The quest for alternative compounds is, therefore, a target for researchers. This work aims to assess the in vitro anthelmintic effect of nifedipine, a calcium channel blocker, and a methanol extract of the flowers of Chrysanthemum coronarium as therapeutic repurposed drugs for treating different developmental stages of Trichinella spiralis in comparison with the reference drug, albendazole. Adult worms and muscle larvae of Trichinella spiralis were incubated with different concentrations of the studied drugs. Drug effects were evaluated by parasitological and electron microscopic examination.As a result, the effects of these drugs on muscle larvae were time and dose-dependent. Moreover, the LC50 after 48 h incubation was 81.25 µg/ml for albendazole, 1.24 µg/ml for nifedipine, and 229.48 µg/ml for C. coronarium. Also, the effects of the tested drugs were prominent on adult worms as the LC50 was 89.77 µg/ml for albendazole, 1.87 µg/ml for nifedipine, and 124.66 µg/ml for C. coronarium. SEM examination of the tegument of T. spiralis adult worms and larvae showed destruction of the adult worms' tegument in all treated groups. The tegument morphological changes were in the form of marked swellings or whole body collapse with the disappearance of internal contents. Furthermore, in silico studies showed that nifedipine might act as a T. spiralis β-tubulin polymerization inhibitor.Our results suggest that nifedipine and C. coronarium extract may be useful therapeutic agents for treating trichinellosis and warrant further assessment in animal disease models.

PMID:37461016 | PMC:PMC10351179 | DOI:10.1186/s12906-023-04076-8

Small. 2023 Jul 17:e2303073. doi: 10.1002/smll.202303073. Online ahead of print.

ABSTRACT

Glioblastoma (GBM), the most aggressive and lethal form of malignant brain tumor, is a therapeutic challenge due to the drug filtration capabilities of the blood-brain barrier (BBB). Interestingly, glioblastoma tends to resist apoptosis during chemotherapy, but is susceptible to ferroptosis. Developing therapies that can effectively target glioblastoma by crossing the BBB and evoke ferroptosis are, therefore, crucial for improving treatment outcomes. Herein, a versatile biomimetic nanoplatform, L-D-I/NPs, is designed that self-assembled by loading the antimalarial drug dihydroartemisinin (DHA) and the photosensitizer indocyanine green (ICG) onto lactoferrin (LF). This nanoplatform can selectively target glioblastoma by binding to low-density lipoprotein receptor-related protein-1 (LRP1) and crossing the BBB, thus inducing glioblastoma cell ferroptosis by boosting intracellular reactive oxygen species (ROS) accumulation and iron overload. In addition, L-D-I/NPs have demonstrated the ability to effectively suppress the progression of orthotopic glioblastoma and significantly prolong survival in a mouse glioblastoma model. This nanoplatform has facilitated the application of non-chemotherapeutic drugs in tumor treatment with minimal adverse effects, paving the way for highly efficient ferroptosis-based therapies for glioblastoma.

PMID:37460404 | DOI:10.1002/smll.202303073

Proc Natl Acad Sci U S A. 2023 Jul 25;120(30):e2221809120. doi: 10.1073/pnas.2221809120. Epub 2023 Jul 17.

ABSTRACT

Early in the COVID-19 pandemic, data suggested that males had a higher risk of developing severe disease and that androgen deprivation therapy might be associated with protection. Combined with the fact that TMPRSS2 (transmembrane serine protease 2), a host entry factor for the SARS-CoV-2 virus, was a well-known androgen-regulated gene, this led to an upsurge of research investigating androgen receptor (AR)-targeting drugs. Proxalutamide, an AR antagonist, was shown in initial clinical studies to benefit COVID-19 patients; however, further validation is needed as one study was retracted. Due to continued interest in proxalutamide, which is in phase 3 trials, we examined its ability to impact SARS-CoV-2 infection and downstream inflammatory responses. Proxalutamide exerted similar effects as enzalutamide, an AR antagonist prescribed for advanced prostate cancer, in decreasing AR signaling and expression of TMPRSS2 and angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 receptor. However, proxalutamide led to degradation of AR protein, which was not observed with enzalutamide. Proxalutamide inhibited SARS-CoV-2 infection with an IC50 value of 97 nM, compared to 281 nM for enzalutamide. Importantly, proxalutamide inhibited infection by multiple SARS-CoV-2 variants and synergized with remdesivir. Proxalutamide protected against cell death in response to tumor necrosis factor alpha and interferon gamma, and overall survival of mice was increased with proxalutamide treatment prior to cytokine exposure. Mechanistically, we found that proxalutamide increased levels of NRF2, an essential transcription factor that mediates antioxidant responses, and decreased lung inflammation. These data provide compelling evidence that proxalutamide can prevent SARS-CoV-2 infection and cytokine-induced lung damage, suggesting that promising clinical data may emerge from ongoing phase 3 trials.

PMID:37459541 | DOI:10.1073/pnas.2221809120

J Biomol Struct Dyn. 2023 Jul 17:1-12. doi: 10.1080/07391102.2023.2236714. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has caused havoc around the globe since 2019 and is considered the largest global epidemic of the twentieth century. Although the first antiviral drug, Remdesivir, was initially introduced against COVID‑19, virtually no tangible therapeutic drugs exist to treat SARS-CoV-2 infection. FDA-approved Paxlovid (Nirmatrelvir supplemented by Ritonavir) was recently announced as a promising drug against the SARS-CoV-2 major protease (Mpro). Here we report for the first time the remarkable inhibitory potentials of lead epigenetic-targeting drugs (epi-drugs) against SARS-CoV-2 Mpro. Epi-drugs are promising compounds to be used in combination with cancer chemotherapeutics to regulate gene expression. The search for all known epi-drugs for the specific inhibition of SARS-CoV-2 Mpro was performed for the first time by consensus (three high-order program) molecular docking studies and end-state free energy calculations. Several epi-drugs were identified with highly comparable binding affinity to SARS-CoV-2 Mpro compared to Nirmatrelvir. In particular, potent histone methyltransferase inhibitor EPZ005687 and DNA methyltransferase inhibitor Guadecitabine were prominent as the most promising epi-drug inhibitors for SARS-CoV-2 Mpro. Long Molecular dynamics (MD) simulations (200 ns each) and corresponding MM-GBSA calculations confirmed the stability of the EPZ005687-Mpro complex with MM-GBSA binding free energy (ΔGbind) -48.2 kcal/mol (EPZ005687) compared to Nirmatrelvir (-44.7 kcal/mol). Taken together, the antiviral activities of the highlighted epi-drugs are reported beyond widespread use in combination with anti-cancer agents. The current findings therefore highlight as yet unexplored antiviral potential of epi-drugs suitable for use in patients struggling with chronic immunosuppressive disorders.Communicated by Ramaswamy H. Sarma.

PMID:37458994 | DOI:10.1080/07391102.2023.2236714