LINCS dataset-based repositioning of rosiglitazone as a potential anti-human adenovirus drug

Discovery of novel HDAC8 inhibitors from natural compounds by in silico high throughput screening

A class I histone deacetylase HDAC8 is associated with several diseases, including cancer, intellectual impairment and parasite infection. Most of the HDAC inhibitors that have so far been found to inhibit HDAC8 limit their efficacy in the clinic by producing toxicities. It is therefore very desirable to develop specific HDAC8 inhibitors. The emergence of HDAC inhibitors derived from natural sources has become quite popular. In recent decades, it has been shown that naturally occurring HDAC inhibitors have strong anticancer properties. A total of 0.2 million natural compounds were screened against HDAC8 from the Universal Natural Product Database (UNPD). Molecular docking was performed for these natural compounds and the top six hits were obtained. In addition, molecular dynamics (MD) simulations were used to evaluate the structural stability and binding affinity of the inhibitors, which showed that the protein-ligand complexes remained stable throughout the 100 ns simulation. MM-PBSA method demonstrated that the selected compounds have high affinity towards HDAC8. We infer from our findings that Hit-1 (-29.35 kcal mol-1), Hit-2 (-29.15 kcal mol-1) and Hit-6 (-30.28 kcal mol-1) have better binding affinity and adhesion to ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics against HDAC8. To compare our discussions and result in an effective way. We performed molecular docking, MD and MM-PBSA analysis for the FDA-approved drug romidepsin. The above results show that our hits show better binding affinity than the compound romidepsin (-12.03 ± 4.66 kcal mol-1). The important hotspot residues Asp29, Ile34, Trp141, Phe152, Asp267, Met274 and Tyr306 have significantly contributed to the protein-ligand interaction. These findings suggest that in vitro testing and additional optimization may lead to the development of HDAC8 inhibitors.Communicated by Ramaswamy H. Sarma.

A quinoline alkaloid potentially modulates the amyloidogenic structural transitions of the biofilm scaffolding small basic protein

Bacterial biofilm formation by communities of opportunistic bacterial pathogens like Staphylococcus epidermidis is regarded as the primary virulence mechanism facilitating the spread of detrimental nosocomial and implant-associated infections. An 18-kDa small basic protein (Sbp) and its amyloid fibrils account for strengthening the biofilm architecture and scaffolding the S. epidermidis biofilm matrix. Our study reports systematic analysis of the amyloidogenic structural transitions of Sbp and predicts the amyloid core of the protein which may trigger misfolding and aggregation. Herein, we report the novel amyloid inhibitory potential of Camptothecin, a quinoline alkaloid which binds stably to Sbp monomers and redirects the formation of unstructured regions further destabilizing the protein. Molecular dynamics simulations reveal that Camptothecin averts β-sheet transitions, interrupts with electrostatic interactions and disrupts the intermolecular hydrophobic associations between the exposed hydrophobic amyloidogenic regions of Sbp. Collectively, our study puts forward the first report detailing the heteromolecular associations and amyloid modulatory effects of Camptothecin which may serve as a structural scaffold for the tailored designing of novel drugs targeting the S. epidermidis biofilm matrix.Communicated by Ramaswamy H. Sarma.

Drug repurposing and molecular mechanisms of the antihypertensive drug candesartan as a TMEM16A channel inhibitor

TMEM16A, a Ca²⁺-activated chloride channel (CaCC), and its pharmacological inhibitors can inhibit the growth of lung adenocarcinoma cells. However,the poor efficacy, safety, and stability of TMEM16A inhibitors limit the development of these agents. Therefore, finding new therapeutic directions from already marketed drugs is a feasible strategy to obtain safe and effective therapeutic drugs. Here, we screened a library contain more than 2400 FDA, EMA, and NMPA-approved drugs through virtual screening. We identified a drug candidate, candesartan (CDST), which showed strong inhibitory effect on the TMEM16A in a concentration-dependent manner with an IC₅₀ of 24.40 ± 3.21 μM. In addition, CDST inhibited proliferation, migration and induced apoptosis of LA795 cells targeting TMEM16A, and significantly inhibited lung adenocarcinoma tumor growth in vivo. The molecular mechanism of CDST inhibiting TMEM16A channel indicated it bound to R515/R535/E623/E624 in the drug pocket, thereby blocked the pore. In conclusion, we identified a novel TMEM16A channel inhibitor, CDST, which exhibited excellent inhibitory activity against lung adenocarcinoma. Considering that CDST has been used in clinical treatment of hypertension, it may play an important role in the combined treatment of hypertension and lung adenocarcinoma as a multi-target drug in the future.

Identification and New Indication of Melanin-Concentrating Hormone Receptor 1 (MCHR1) Antagonist Derived from Machine Learning and Transcriptome-Based Drug Repositioning Approaches

Melanin-concentrating hormone receptor 1 (MCHR1) has been a target for appetite suppressants, which are helpful in treating obesity. However, it is challenging to develop an MCHR1 antagonist because its binding site is similar to that of the human Ether-à-go-go-Related Gene (hERG) channel, whose inhibition may cause cardiotoxicity. Most drugs developed as MCHR1 antagonists have failed in clinical development due to cardiotoxicity caused by hERG inhibition. Machine learning-based prediction models can overcome these difficulties and provide new opportunities for drug discovery. In this study, we identified KRX-104130 with potent MCHR1 antagonistic activity and no cardiotoxicity through virtual screening using two MCHR1 binding affinity prediction models and an hERG-induced cardiotoxicity prediction model. In addition, we explored other possibilities for expanding the new indications for KRX-104130 using a transcriptome-based drug repositioning approach. KRX-104130 increased the expression of low-density lipoprotein receptor (LDLR), which induced cholesterol reduction in the gene expression analysis. This was confirmed by comparison with gene expression in a nonalcoholic steatohepatitis (NASH) patient group. In a NASH mouse model, the administration of KRX-104130 showed a protective effect by reducing hepatic lipid accumulation, liver injury, and histopathological changes, indicating a promising prospect for the therapeutic effect of NASH as a new indication for MCHR1 antagonists.

Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach

Drug repositioning research using transcriptome data has recently attracted attention. In this study, we attempted to identify new target proteins of the urotensin-II receptor antagonist, KR-37524 (4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)piperazine-1-carboxamide dihydrochloride), using a transcriptome-based drug repositioning approach. To do this, we obtained KR-37524-induced gene expression profile changes in four cell lines (A375, A549, MCF7, and PC3), and compared them with the approved drug-induced gene expression profile changes available in the LINCS L1000 database to identify approved drugs with similar gene expression profile changes. Here, the similarity between the two gene expression profile changes was calculated using the connectivity score. We then selected proteins that are known targets of the top three approved drugs with the highest connectivity score in each cell line (12 drugs in total) as potential targets of KR-37524. Seven potential target proteins were experimentally confirmed using an in vitro binding assay. Through this analysis, we identified that neurologically regulated serotonin transporter proteins are new target proteins of KR-37524. These results indicate that the transcriptome-based drug repositioning approach can be used to identify new target proteins of a given compound, and we provide a standalone software developed in this study that will serve as a useful tool for drug repositioning.

Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery

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.

Antiviral activity of Mulberroside C against enterovirus A71 in vitro and in vivo

Enterovirus A71 (EV-A71) is one of the main causative agents of hand, foot and mouth disease which seriously threatens young children's health and lives. However, there is no effective therapy currently available for treating these infections. Therefore, effective drugs to prevent and treat EV-A71 infections are urgently needed. Here, we identified Mulberroside C potently against the proliferation of EV-A71. The in-vitro anti-EV-A71 activity of Mulberroside C was assessed by cytopathic effect inhibition and viral plaque reduction assays, and the results showed that Mulberroside C significantly inhibited EV-A71 infection. The downstream assays affirmed that Mulberroside C inhibited viral protein and RNA synthesis. Furthermore, Mulberroside C effectively reduced clinical symptoms in EV-A71 infected mice and reduced mortality at higher concentrations. The mechanism study indicated that Mulberroside C bound to the hydrophobic pocket of viral capsid protein VP1, thereby preventing viral uncoating and genome release. Taken together, our study indicated that Mulberroside C could be a promising EV-A71 inhibitor and worth extensive preclinical investigation as a lead compound.