Brief Bioinform. 2021 Aug 10:bbab319. doi: 10.1093/bib/bbab319. Online ahead of print.

ABSTRACT

In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug-drug similarity, disease-disease similarity and drug-disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer's disease (e.g. benzatropine) and Parkinson's disease (e.g. trihexyphenidyl and haloperidol).

PMID:34378011 | DOI:10.1093/bib/bbab319

Neurooncol Adv. 2021 Jun 24;3(1):vdab075. doi: 10.1093/noajnl/vdab075. eCollection 2021 Jan-Dec.

ABSTRACT

BACKGROUND: The dismal prognosis of glioblastoma (GBM) may be related to the ability of GBM cells to develop mechanisms of treatment resistance. We designed a protocol called Coordinated Undermining of Survival Paths combining 9 repurposed non-oncological drugs with metronomic temozolomide-version 3-(CUSP9v3) to address this issue. The aim of this phase Ib/IIa trial was to assess the safety of CUSP9v3.

METHODS: Ten adults with histologically confirmed GBM and recurrent or progressive disease were included. Treatment consisted of aprepitant, auranofin, celecoxib, captopril, disulfiram, itraconazole, minocycline, ritonavir, and sertraline added to metronomic low-dose temozolomide. Treatment was continued until toxicity or progression. Primary endpoint was dose-limiting toxicity defined as either any unmanageable grade 3-4 toxicity or inability to receive at least 7 of the 10 drugs at ≥ 50% of the per-protocol doses at the end of the second treatment cycle.

RESULTS: One patient was not evaluable for the primary endpoint (safety). All 9 evaluable patients met the primary endpoint. Ritonavir, temozolomide, captopril, and itraconazole were the drugs most frequently requiring dose modification or pausing. The most common adverse events were nausea, headache, fatigue, diarrhea, and ataxia. Progression-free survival at 12 months was 50%.

CONCLUSIONS: CUSP9v3 can be safely administered in patients with recurrent GBM under careful monitoring. A randomized phase II trial is in preparation to assess the efficacy of the CUSP9v3 regimen in GBM.

PMID:34377985 | PMC:PMC8349180 | DOI:10.1093/noajnl/vdab075

Comput Struct Biotechnol J. 2021 Jul 10;19:3990-4002. doi: 10.1016/j.csbj.2021.07.007. eCollection 2021.

ABSTRACT

Despite the fact that an increased amount of survival-related lncRNAs have been found in cancer, few drugs that target lncRNAs are approved for treatment. Here, we developed a network-based algorithm, LncTx, to repurpose the medications that potentially act on survival-related lncRNAs in lung cancer. We used eight survival-related lncRNAs derived from our previous study to test the efficacy of this method. LncTx calculates the shortest path length (proximity) between the drug targets and the lncRNA-correlated proteins in the protein-protein interaction network (interactome). LncTx contains seven different proximity measures, which are calculated in the unweighted or weighted interactome. First, to test the performance of LncTx in predicting correct indication of drugs, we benchmarked the proximity measures based on the accuracy of differentiating anticancer drugs from non-anticancer drugs. The closest proximity weighted by clustering coefficient (closestCC) has the best performance (AUC around 0.8) compared to other proximity measures across all survival-related lncRNAs. The majority of the other six proximity measures have decent performance as well, with AUC greater than 0.7. Second, to evaluate whether LncTx can repurpose the drugs effectively acting on the lncRNAs, we clustered the drugs according to their proximities by hierarchical clustering. The drugs with smaller proximity (proximal drugs) were proved to be more effective than the drugs with larger proximity (distal drugs). In conclusion, LncTx enables us to accurately identify anticancer drugs and can potentially be an index to repurpose effective agents acting on survival-related lncRNAs in lung cancer.

PMID:34377365 | PMC:PMC8319574 | DOI:10.1016/j.csbj.2021.07.007

Sci Rep. 2021 Aug 10;11(1):16251. doi: 10.1038/s41598-021-95507-4.

ABSTRACT

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.

PMID:34376738 | DOI:10.1038/s41598-021-95507-4

Comput Biol Med. 2021 Jul 29;136:104676. doi: 10.1016/j.compbiomed.2021.104676. Online ahead of print.

ABSTRACT

Analysis and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, as well as drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, while providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by a number of features, namely, pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad composition, transition and distribution, Moreau-Broto autocorrelation, and structural features. The drug compounds are subsequently encoded using substructure fingerprints. Next, eXtreme gradient boosting (XGBoost) is used to determine the subset of non-redundant features of importance. The optimal balanced set of sample vectors is obtained by applying the synthetic minority oversampling technique (SMOTE). Finally, a DTIs predictor, DNN-DTIs, is developed based on a deep neural network (DNN) via a layer-by-layer learning scheme. Experimental results indicate that DNN-DTIs achieves better performance than other state-of-the-art predictors with ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's datasets. Therefore, the accurate prediction performance of DNN-DTIs makes it a favored choice for contributing to the study of DTIs, especially drug repositioning.

PMID:34375902 | DOI:10.1016/j.compbiomed.2021.104676

IEEE/ACM Trans Comput Biol Bioinform. 2021 Aug 10;PP. doi: 10.1109/TCBB.2021.3103595. Online ahead of print.

ABSTRACT

Drug repositioning/repurposing is a very important approach towards identifying novel treatments for diseases in drug discovery. Recently, large-scale biological datasets are increasingly available for pharmaceutical research and promote the development of drug repositioning, but efficiently utilizing these datasets remains challenging. In this paper, we develop a novel multimodal framework, termed GraphPK(Graph-based Prior Knowledge) for improving in silico drug repositioning via using the prior knowledge from a drug knowledge graph. First, we construct a knowledge graph by integrating relevant bio-entities and associations/interactions among them, and apply the knowledge graph embedding technique to extract prior knowledge of drugs and diseases. Moreover, we make use of the known drug-disease association, and obtain known association-based features from an association bipartite graph through graph embedding, and also take into account biological domain features. Finally, we design a multimodal neural network to combine three types of features and build the prediction model.Massive experiments show that our method outperforms other state-of-the-art methods, and the ablation analysis reveals that prior knowledge from knowledge graphs can not only lift the predictive power of drug repositioning, but also enhance the models robustness to different scenarios. The results of case studies offer support that GraphPK has the potential of actual use.

PMID:34375284 | DOI:10.1109/TCBB.2021.3103595

Assay Drug Dev Technol. 2021 Aug 10. doi: 10.1089/adt.2021.014. Online ahead of print.

ABSTRACT

Recent reports have highlighted the possible role of the antipsychotic chlorpromazine and the antidepressant fluvoxamine as anti-coronavirus disease 2019 (COVID-19) agents. The objective of this narrative review is to explore what is known about the activity of psychotropic medications against viruses in addition to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). PubMed was queried for "drug repurposing, antiviral activity," and for "antiviral activity" with "psychotropic drugs" and individual agents, through November 2020. Of more than 100 psychotropic agents, 37 drugs, including 27 with a history of pediatric use were identified, which had been studied in the preclinical setting and found to have activity against viruses which are human pathogens. Effects were evaluated by type of virus and by category of psychotropic agent. Activity was identified both against viruses known to cause epidemics such as SARS-CoV-2 and Ebola and against those that are the cause of rare disorders such as Human Papillomatosis Virus-related respiratory papillomatosis. Individual drugs and classes of psychotropics often had activity against multiple viruses, with promiscuity explained by shared viral or cellular targets. Safety profiles of psychotropics may be more tolerable in this context than when they are used long-term in the setting of psychiatric illness. Nonetheless, translation of in vitro results to the clinical arena has been slow. Psychotropic medications as a class deserve further study, including in clinical trials for repurposing as antiviral drugs for children and adults.

PMID:34375133 | DOI:10.1089/adt.2021.014

Nat Rev Neurol. 2021 Aug 9. doi: 10.1038/s41582-021-00550-4. Online ahead of print.

NO ABSTRACT

PMID:34373635 | DOI:10.1038/s41582-021-00550-4

mBio. 2021 Aug 10:e0145821. doi: 10.1128/mBio.01458-21. Online ahead of print.

ABSTRACT

Aspergillus fumigatus is an important fungal pathogen and the main etiological agent of aspergillosis, a disease characterized by a noninvasive process that can evolve to a more severe clinical manifestation, called invasive pulmonary aspergillosis (IPA), in immunocompromised patients. The antifungal arsenal to threat aspergillosis is very restricted. Azoles are the main therapeutic approach to control IPA, but the emergence of azole-resistant A. fumigatus isolates has significantly increased over recent decades. Therefore, new strategies are necessary to combat aspergillosis, and drug repurposing has emerged as an efficient and alternative approach for identifying new antifungal drugs. Here, we used a screening approach to analyze A. fumigatus in vitro susceptibility to 1,127 compounds. A. fumigatus was susceptible to 10 compounds, including miltefosine, a drug that displayed fungicidal activity against A. fumigatus. By screening an A. fumigatus transcription factor null library, we identified a single mutant, which has the smiA (sensitive to miltefosine) gene deleted, conferring a phenotype of susceptibility to miltefosine. The transcriptional profiling (RNA-seq) of the wild-type and ΔsmiA strains and chromatin immunoprecipitation coupled to next-generation sequencing (ChIP-Seq) of an SmiA-tagged strain exposed to miltefosine revealed genes of the sphingolipid pathway that are directly or indirectly regulated by SmiA. Sphingolipid analysis demonstrated that the mutant has overall decreased levels of sphingolipids when growing in the presence of miltefosine. The identification of SmiA represents the first genetic element described and characterized that plays a direct role in miltefosine response in fungi. IMPORTANCE The filamentous fungus Aspergillus fumigatus causes a group of diseases named aspergillosis, and their development occurs after the inhalation of conidia dispersed in the environment. Very few classes of antifungal drugs are available for aspergillosis treatment, e.g., azoles, but the emergence of global resistance to azoles in A. fumigatus clinical isolates has increased over recent decades. Repositioning or repurposing drugs already available on the market is an interesting and faster opportunity for the identification of novel antifungal agents. By using a repurposing strategy, we identified 10 different compounds that impact A. fumigatus survival. One of these compounds, miltefosine, demonstrated fungicidal activity against A. fumigatus. The mechanism of action of miltefosine is unknown, and, aiming to get more insights about it, we identified a transcription factor, SmiA (sensitive to miltefosine), important for miltefosine resistance. Our results suggest that miltefosine displays antifungal activity against A. fumigatus, interfering in sphingolipid biosynthesis.

PMID:34372704 | DOI:10.1128/mBio.01458-21

Polymers (Basel). 2021 Jul 30;13(15):2530. doi: 10.3390/polym13152530.

ABSTRACT

Recently, the antitumor potential of benzimidazole anthelmintics, such as mebendazole and its analogues, have been reported to have minimal side effects, in addition to their well-known anti-parasitic abilities. However, their administration is strongly limited owing to their extremely poor solubility, which highly depletes their overall bioavailability. This study describes the design, synthesis, and physico-chemical properties of polymer-mebendazole nanomedicines for drug repurposing in cancer therapy. The conjugation of mebendazole to water-soluble and biocompatible polymer carrier was carried out via biodegradable bond, relying on the hydrolytic action of lysosomal hydrolases for mebendazole release inside the tumor cells. Five low-molecular-weight mebendazole derivatives, differing in their inner structure, and two polymer conjugates differing in their linker structure, were synthesized. The overall synthetic strategy was designed to enable the modification and polymer conjugation of most benzimidazole-based anthelmintics, such as albendazole, fenbendazole or albendazole, besides the mebendazole. Furthermore, the described methodology may be suitable for conjugation of other biologically active compounds with a heterocyclic N-H group in their molecules.

PMID:34372133 | DOI:10.3390/polym13152530

Curr Drug Targets. 2021 Aug 8. doi: 10.2174/1389450122666210809090909. Online ahead of print.

ABSTRACT

Since November 2019, the new Coronavirus disease (COVID-19) caused by the etiological agent SARS-CoV-2 has been responsible for several cases worldwide, becoming pandemic in March 2020. Pharmaceutical industries and academics have joined their efforts to discover new therapies to control the disease, since there are no specific drugs to combat this emerging virus. Thus, several targets have been explored, among them the transmembrane protease serine 2 (TMPRSS2) has gained greater interest in the scientific community. In this context, this review will describe the importance of TMPRSS2 protease and the significant advances in virtual screening focused on discovering new inhibitors. In this review, it was observed that molecular modeling methods could be powerful tools in identifying new molecules against SARS-CoV-2. Thus, this review could be used to guide researchers worldwide to explore the biological and clinical potential of compounds that could be promising drug candidates against SARS-CoV-2, acting by inhibition of TMPRSS2 protein.

PMID:34370633 | DOI:10.2174/1389450122666210809090909

J Biomol Struct Dyn. 2021 Aug 9:1-18. doi: 10.1080/07391102.2021.1958700. Online ahead of print.

ABSTRACT

The current outbreak of COVID-19 is leading an unprecedented scientific effort focusing on targeting SARS-CoV-2 proteins critical for its viral replication. Herein, we performed high-throughput virtual screening of more than eleven thousand FDA-approved drugs using backpropagation-based artificial neural networks (q2LOO = 0.60, r2 = 0.80 and r2pred = 0.91), partial-least-square (PLS) regression (q2LOO = 0.83, r2 = 0.62 and r2pred = 0.70) and sequential minimal optimization (SMO) regression (q2LOO = 0.70, r2 = 0.80 and r2pred = 0.89). We simulated the stability of Acarbose-derived hexasaccharide, Naratriptan, Peramivir, Dihydrostreptomycin, Enviomycin, Rolitetracycline, Viomycin, Angiotensin II, Angiotensin 1-7, Angiotensinamide, Fenoterol, Zanamivir, Laninamivir and Laninamivir octanoate with 3CLpro by 100 ns and calculated binding free energy using molecular mechanics combined with Poisson-Boltzmann surface area (MM-PBSA). Our QSAR models and molecular dynamics data suggest that seven repurposed-drug candidates such as Acarbose-derived Hexasaccharide, Angiotensinamide, Dihydrostreptomycin, Enviomycin, Fenoterol, Naratriptan and Viomycin are potential SARS-CoV-2 main protease inhibitors. In addition, our QSAR models and molecular dynamics simulations revealed that His41, Asn142, Cys145, Glu166 and Gln189 are potential pharmacophoric centers for 3CLpro inhibitors. Glu166 is a potential pharmacophore for drug design and inhibitors that interact with this residue may be critical to avoid dimerization of 3CLpro. Our results will contribute to future investigations of novel chemical scaffolds and the discovery of novel hits in high-throughput screening as potential anti-SARS-CoV-2 properties.Communicated by Ramaswamy H. Sarma.

PMID:34370631 | DOI:10.1080/07391102.2021.1958700

Chem Biodivers. 2021 Aug 9. doi: 10.1002/cbdv.202100336. Online ahead of print.

ABSTRACT

Leishmaniasis is a group of tropical diseases caused by parasitic protozoa belonging to the genus Leishmania. The disease is categorized in cutaneous leishmaniasis (CL), mucocutaneous leishmaniasis (MCL), and visceral leishmaniasis (VL). The conventional treatment is complex and can present high toxicity and therapeutic failures. Thus, there is a continuing need to develop new treatments. In this review, we focus on the novel molecules described in the literature with potential leishmanicidal activity, categorizing them in pre-clinical (in vitro, in vivo), drug repurposing and clinical research.

PMID:34369662 | DOI:10.1002/cbdv.202100336

Brief Bioinform. 2021 Aug 7:bbab289. doi: 10.1093/bib/bbab289. Online ahead of print.

ABSTRACT

The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great interest to find an appropriate data representation and modeling method to facilitate these studies. The anatomical therapeutic chemical (ATC) classification system, proposed by the World Health Organization (WHO), is an essential source of information for drug repurposing and discovery. Besides, computational methods are applied to predict drug ATC classification. We conducted a systematic review of ATC computational prediction studies and revealed the differences in data sets, data representation, algorithm approaches, and evaluation metrics. We then proposed a deep fusion learning (DFL) framework to optimize the ATC prediction model, namely DeepATC. The methods based on graph convolutional network, inferring biological network and multimodel attentive fusion network were applied in DeepATC to extract the molecular topological information and low-dimensional representation from the molecular graph and heterogeneous biological networks. The results indicated that DeepATC achieved superior model performance with area under the curve (AUC) value at 0.968. Furthermore, the DFL framework was performed for the transcriptome data-based ATC prediction, as well as another independent task that is significantly relevant to drug discovery, namely drug-target interaction. The DFL-based model achieved excellent performance in the above-extended validation task, suggesting that the idea of aggregating the heterogeneous biological network and node's (molecule or protein) self-topological features will bring inspiration for broader drug repurposing and discovery research.

PMID:34368838 | DOI:10.1093/bib/bbab289

Front Cell Infect Microbiol. 2021 Jul 23;11:698662. doi: 10.3389/fcimb.2021.698662. eCollection 2021.

ABSTRACT

Scedosporium and Lomentospora species are filamentous fungi responsible for a wide range of infections in humans and are frequently associated with cystic fibrosis and immunocompromising conditions. Because they are usually resistant to many antifungal drugs available in clinical settings, studies of alternative targets in fungal cells and therapeutic approaches are necessary. In the present work, we evaluated the in vitro antifungal activity of miltefosine against Scedosporium and Lomentospora species and how this phospholipid analogue affects the fungal cell. Miltefosine inhibited different Scedosporium and Lomentospora species at 2-4 µg/ml and reduced biofilm formation. The loss of membrane integrity in Scedosporium aurantiacum caused by miltefosine was demonstrated by leakage of intracellular components and lipid raft disorganisation. The exogenous addition of glucosylceramide decreased the inhibitory activity of miltefosine. Reactive oxygen species production and mitochondrial activity were also affected by miltefosine, as well as the susceptibility to fluconazole, caspofungin and myoricin. The data obtained in the present study contribute to clarify the dynamics of the interaction between miltefosine and Scedosporium and Lomentospora cells, highlighting its potential use as new antifungal drug in the future.

PMID:34368017 | PMC:PMC8343104 | DOI:10.3389/fcimb.2021.698662

Front Oncol. 2021 Jul 22;11:711225. doi: 10.3389/fonc.2021.711225. eCollection 2021.

ABSTRACT

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

PMID:34367996 | PMC:PMC8340770 | DOI:10.3389/fonc.2021.711225

Front Microbiol. 2021 Jul 21;12:694534. doi: 10.3389/fmicb.2021.694534. eCollection 2021.

ABSTRACT

Because of the catastrophic outbreak of global coronavirus disease 2019 (COVID-19) and its strong infectivity and possible persistence, computational repurposing of existing approved drugs will be a promising strategy that facilitates rapid clinical treatment decisions and provides reasonable justification for subsequent clinical trials and regulatory reviews. Since the effects of a small number of conditionally marketed vaccines need further clinical observation, there is still an urgent need to quickly and effectively repurpose potentially available drugs before the next disease peak. In this work, we have manually collected a set of experimentally confirmed virus-drug associations through the publicly published database and literature, consisting of 175 drugs and 95 viruses, as well as 933 virus-drug associations. Then, because the samples are extremely sparse and unbalanced, negative samples cannot be easily obtained. We have developed an ensemble model, EMC-Voting, based on matrix completion and weighted soft voting, a semi-supervised machine learning model for computational drug repurposing. Finally, we have evaluated the prediction performance of EMC-Voting by fivefold crossing-validation and compared it with other baseline classifiers and prediction models. The case study for the virus SARS-COV-2 included in the dataset demonstrates that our model achieves the outperforming AUPR value of 0.934 in virus-drug association's prediction.

PMID:34367094 | PMC:PMC8334363 | DOI:10.3389/fmicb.2021.694534

Drug Des Devel Ther. 2021 Jul 30;15:3313-3330. doi: 10.2147/DDDT.S320320. eCollection 2021.

ABSTRACT

The acute respiratory syndrome coronavirus (SARS-CoV-2) has spread across the world, resulting in a pandemic COVID-19 which is a human zoonotic disease that is caused by a novel coronavirus (CoV) strain thought to have originated in wild or captive bats in the initial COVID outbreak region. The global COVID-19 outbreak started in Guangdong Province, China's southernmost province. The global response to the COVID-19 pandemic has been hampered by the sheer number of infected people, many of whom need intensive care before succumbing to the disease. The epidemic is being handled by a combination of disease control by public health interventions and compassionate treatment for those who have been impacted. There is no clear anti-COVID-19 medication available at this time. However, the need to find medications that can turn the tide has led to the development of a number of investigational drugs as potential candidates for improving outcomes, especially in the severely and critically ill. Although many of these adjunctive medications are still being studied in clinical trials, professional organizations have attempted to define the circumstances in which their use is deemed off-label or compassionate. It is important to remind readers that new information about COVID-19's clinical features, treatment options, and outcomes is released on a regular basis. The mainstay of treatment remains optimized supportive care, and the therapeutic effectiveness of the subsequent agents is still being studied.

PMID:34366663 | PMC:PMC8335551 | DOI:10.2147/DDDT.S320320

EBioMedicine. 2021 Aug 5;70:103510. doi: 10.1016/j.ebiom.2021.103510. Online ahead of print.

ABSTRACT

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) remains one of the deadly cancer types. Comprehensively dissecting the molecular characterization and the heterogeneity of ESCC paves the way for developing more promising therapeutics.

METHODS: Expression profiles of multiple ESCC datasets were integrated. ATAC-seq and RNA-seq were combined to reveal the chromatin accessibility features. A prognosis-related subtype classifier (PrSC) was constructed, and its association with the tumor microenvironment (TME) and immunotherapy was assessed. The key gene signature was validated in clinical samples. Based on the TME heterogeneity of ESCC patients, potential subtype-specific therapeutic agents were screened.

FINDINGS: The common differentially expressed genes (cDEGs) in ESCC were identified. Up-regulated genes (HEATR1, TIMELESS, DTL, GINS1, RUVBL1, and ECT2) were found highly important in ESCC cell survival. The expression alterations of PRIM2, HPGD, NELL2, and TFAP2B were associated with chromatin accessibility changes. PrSC was a robust scoring tool that was not only associated with the prognosis of ESCC patients, but also could reflect the TME heterogeneity. TNS1high fibroblasts were associated with immune exclusion. TG-101348 and Vinorelbine were identified as potential subtype-specific therapeutic agents. Besides, the application of PrSC into two immunotherapy cohorts indicated its potential value in assessing treatment response to immunotherapy.

INTERPRETATION: Our study depicted the multi-dimensional characterization of ESCC, established a robust scoring tool for the prognosis assessment, highlighted the role of TNS1high fibroblasts in TME, and identified potential drugs for clinical use.

FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

PMID:34365093 | DOI:10.1016/j.ebiom.2021.103510

Biochem Biophys Res Commun. 2021 Aug 4;572:138-144. doi: 10.1016/j.bbrc.2021.07.076. Online ahead of print.

ABSTRACT

Heart diseases are known as the most primary causes of mortality worldwide. Although many therapeutic approaches and medications are proposed for these diseases, the identification of novel therapeutics in fatal heart conditions is promptly demanded. Besides, the interplay between gene expression data and molecular docking provides several novel insights to discover more effective and specific drugs for the treatment of the diseases. This study aimed to discover potent therapeutic drugs in the heart diseases based on the expression profile of heart-specific genes exclusively. Initially, the heart-specific and highly expressed genes were identified by comparing the gene expression profile of different body tissues. Subsequently, the druggable-genes were identified using in silico techniques. The interaction between these druggable genes with more than 1600 FDA approved drugs was then investigated using the molecular docking simulation. By comprehensively analyzing RNA-sequencing data obtained from 949 normal tissue samples, 48 heart-specific genes were identified in both the heart development and function. Notably, of these, 24 heart-specific genes were capable to be considered as druggable genes, among which only MYBPC3, MYLK3, and SCN5A genes entered the molecular docking process due to their functions. Afterward, the pharmacokinetics properties of top 10 ligands with the highest binding affinity for these proteins were studied. Accordingly, methylergonovine, fosaprepitant, pralatrexate, daunorubicin, glecaprevir, digoxin, and venetoclax drugs were competent, in order to interact with the target proteins perfectly. It was shown that these medications can be used as specific drugs for the treatment of heart diseases after fulfilling further experiments in this regard.

PMID:34364293 | DOI:10.1016/j.bbrc.2021.07.076