Abstract: The influenza virus is a constantly evolving pathogen that challenges medical and public health systems. Traditionally, curcumin has been used to treat airway inflammatory diseases, such as bronchitis and pneumonia. To elucidate common targets of curcumin and influenza infection and underlying mechanisms, we employed network pharmacology and molecular docking approaches and confirmed results using in vitro experiments. Biological targets of curcumin and influenza were collected, and potential targets were identified by constructing compound-disease target (C-D) and protein-protein interaction (PPI) networks. The ligand-target interaction was determined using the molecular docking method, and in vitro antiviral experiments and target confirmation were conducted to evaluate curcumin's effects on influenza. Our network and pathway analyses implicated the four targets of AKT1, RELA, MAPK1, and TP53 that could be involved in the inhibitory effects of curcumin on influenza. The binding energy calculations of each ligand-target interaction in the molecular docking showed that curcumin bound to AKT1 with the highest affinity among the four targets. In vitro experiments, in which influenza virus-infected MDCK cells were pre-, co-, or post-treated with curcumin, confirmed curcumin's prophylactic and therapeutic effects. Influenza virus induction increased the level of mRNA expression of AKT in MDCK cells, and the level was attenuated by curcumin treatment. Collectively, our findings identified potential targets of curcumin against influenza and suggest curcumin as a potential therapy for influenza infection.

Abstract: With the gradual increase in the COVID-19 mortality rate, there is an urgent need for an effective drug/vaccine. Several drugs like Remdesivir, Azithromycin, Favirapir, Ritonavir, Darunavir, etc., are put under evaluation in more than 300 clinical trials to treat COVID-19. On the other hand, several vaccines like Pfizer-BioNTech, Moderna, Johnson & Johnson's Janssen, Sputnik V, Covishield, Covaxin, etc., also evolved from the research study. While few of them already gets approved, others show encouraging results and are still under assessment. In parallel, there are also significant developments in new drug development. But, since the approval of new molecules takes substantial time, drug repurposing studies have also gained considerable momentum. The primary agent of the disease progression of COVID-19 is SARS-CoV2/nCoV, which is believed to have approximately 89% genetic resemblance with SARS-CoV, a coronavirus responsible for the massive outbreak in 2003. With this hypothesis, Human-SARS-CoV protein interactions are used to develop an in-silico Human-nCoV network by identifying potential COVID-19 human spreader proteins by applying the SIS model and fuzzy thresholding by a possible COVID-19 FDA drugs target-based validation. At first, the complete list of FDA drugs is identified for the level-1 and level-2 spreader proteins in this network, followed by applying a drug consensus scoring strategy. The same consensus strategy is involved in the second analysis but on a curated overlapping set of key genes/proteins identified from COVID-19 symptoms. Validation using subsequent docking study has also been performed on COVID-19 potential drugs with the available major COVID-19 crystal structures whose PDB IDs are: 6LU7, 6M2Q, 6W9C, 6M0J, 6M71 and 6VXX. Our computational study and docking results suggest that Fostamatinib (R406 as its active promoiety) may also be considered as one of the potential candidates for further clinical trials in pursuit to counter the spread of COVID-19.

Abstract: Bedaquiline is used to treat multi-resistant tuberculosis in adults. The fumarate salt is commercially available and used in the product Sirturo. To provide open access to bedaquiline molecule once the patent on the chemical substance expires, new salts were screened. This work offers additional information on the bedaquiline system, as new salts may present better pharmacokinetic properties. The current studies focus on the attempted isolation of the acetate, benzoate, benzenesulfonate, hydrobromide, succinate, hydrochloride, tartrate, lactate, maleate, malate, and mesylate salts of bedaquiline. Potential salts were screened using a unique combination of conventional screening, and small-scale experiments supplemented by crystallographic analysis and infrared microspectroscopy. Salts were prepared on a larger scale by dissolving 1:1 ratios of the individual salt formers and bedaquiline base (30 mg, 0.055 mmol) in different solvents and allowing the solutions to evaporate or crystallize. X-ray diffraction (XRD) techniques and spectroscopic and thermal analyses were employed to characterize the salts. The benzoate and maleate salts were selected as lead candidates after reviewing preliminary characterization data. To determine the most stable forms for the leads, a polymorph screen was conducted using solvents of various polarities. These salt screens successfully generated five new salts of bedaquiline, namely, benzoate, maleate, hydrochloride, besylate, and mesylate. The existence of these salts was confirmed by powder XRD, proton NMR, and IR spectroscopies. TGA and DSC thermal analysis along with hot-stage optical microscopy were further used to characterize the salts. The polymorph screen conducted on the salts suggested the absence of additional polymorphs at 1 g scale.

Abstract: OBJECTIVE: To identify specific Chinese medicines (CM) that may benefit patients with primary liver cancer (PLC), and to explore the mechanism of action of these medicines. METHODS: In this retrospective, singlecenter study, prescription information from PLC patients was used in combination with Traditional Chinese Medicine Inheritance Supports System to identify the specific core drugs. A system pharmacology approach was employed to explore the mechanism of action of these medicines. RESULTS: Taking CM more than 6 months was significantly associated with improved survival outcomes. In total, 77 putative targets and 116 bioactive ingredients of the core drugs were identified and included in the analysis (P<0.05). A total of 1,036 gene ontology terms were found to be enriched in PLC. A total of 75 pathways identified from Kyoto Encyclopedia of Genes and Genomes were also enriched in this disease, including fluid shear stress, interleukin-17 signaling, signaling between advanced glycan end products and their receptors, cellular senescence, tumor necrosis factor signaling, p53 signaling, cell cycle signaling, steroid hormone biosynthesis, T-helper 17 cell differentiation, and metabolism of xenobiotics by cytochrome. Docking studies suggested that the ingredients in the core drugs exert therapeutic effects in PLC by modulating c-Jun and interleukin-6. CONCLUSIONS: Receiving CM for 6 months or more improves survival for the patients with PLC. The core drugs that really benefit for PLC patients likely regulates the tumor microenvironment and tumor itself.

Abstract: In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Abstract: 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.

Abstract: BACKGROUND: We performed in silico prediction of the interactions between compounds of Jamu herbs and human proteins by utilizing data-intensive science and machine learning methods. Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates. METHODS: Initially, data related to compounds, target proteins, and interactions between them were collected from open access databases. Compounds are represented by molecular fingerprints, whereas amino acid sequences are represented by numerical protein descriptors. Then, prediction models that predict the interactions between compounds and target proteins were constructed using support vector machine and random forest. RESULTS: A random forest model constructed based on MACCS fingerprint and amino acid composition obtained the highest accuracy. We used the best model to predict target proteins for 94 important Jamu compounds and assessed the results by supporting evidence from published literature and other sources. There are 27 compounds that can be validated by professional doctors, and those compounds belong to seven efficacy groups. CONCLUSION: By comparing the efficacy of predicted compounds and the relations of the targeted proteins with diseases, we found that some compounds might be considered as drug candidates.

Abstract: BACKGROUND: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue. METHODS: We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles. RESULTS: Our proposed DDI prediction model provides multiple advantages: (1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, (2) it offers model explainability via an Attention mechanism for identifying salient input features and (3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources. CONCLUSIONS: We find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability.

Abstract: COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

Abstract: BACKGROUND: Proinflammatory cytokines are correlated with the severity of disease in patients with COVID-19. IL6-mediated activation of STAT3 proliferates proinflammatory responses that lead to cytokine storm promotion. Thus, STAT3 inhibitors may play a crucial role in managing the COVID-19 pathogenesis. The present study discusses a method for predicting inhibitors against the STAT3 signaling pathway. METHOD: The main dataset comprises 1565 STAT3 inhibitors and 1671 non-inhibitors used for training, testing, and evaluation of models. A number of machine learning classifiers have been implemented to develop the models. RESULTS: The outcomes of the data analysis show that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors. First, we developed models using 2-D and 3-D chemical descriptors and achieved a maximum AUC of 0.84 and 0.73, respectively. Second, fingerprints are used to build predictive models and achieved 0.86 AUC with an accuracy of 78.70% on the validation dataset. Finally, models were developed using hybrid descriptors, which achieved a maximum of 0.87 AUC with 78.55% accuracy on the validation dataset. CONCLUSION: We used the best model to identify STAT3 inhibitors in FDA-approved drugs and found few drugs (e.g., Tamoxifen and Perindopril) to manage the cytokine storm in COVID-19 patients. A webserver "STAT3In" (https://webs.iiitd.edu.in/raghava/stat3in/) has been developed to predict and design STAT3 inhibitors.