Front Oncol. 2023 Jan 17;12:1075559. doi: 10.3389/fonc.2022.1075559. eCollection 2022.

ABSTRACT

Glioblastoma (GBM) remains a cancer of high unmet clinical need. Current standard of care for GBM, consisting of maximal surgical resection, followed by ionisation radiation (IR) plus concomitant and adjuvant temozolomide (TMZ), provides less than 15-month survival benefit. Efforts by conventional drug discovery to improve overall survival have failed to overcome challenges presented by inherent tumor heterogeneity, therapeutic resistance attributed to GBM stem cells, and tumor niches supporting self-renewal. In this review we describe the steps academic researchers are taking to address these limitations in high throughput screening programs to identify novel GBM combinatorial targets. We detail how they are implementing more physiologically relevant phenotypic assays which better recapitulate key areas of disease biology coupled with more focussed libraries of small compounds, such as drug repurposing, target discovery, pharmacologically active and novel, more comprehensive anti-cancer target-annotated compound libraries. Herein, we discuss the rationale for current GBM combination trials and the need for more systematic and transparent strategies for identification, validation and prioritisation of combinations that lead to clinical trials. Finally, we make specific recommendations to the preclinical, small compound screening paradigm that could increase the likelihood of identifying tractable, combinatorial, small molecule inhibitors and better drug targets specific to GBM.

PMID:36733367 | PMC:PMC9886867 | DOI:10.3389/fonc.2022.1075559

Anticancer Agents Med Chem. 2023 Feb 2. doi: 10.2174/1871520623666230202163112. Online ahead of print.

ABSTRACT

Glioblastoma multiforme (GBM) is an aggressive form of adult brain tumor that can arise from a low-grade astrocytoma. In recent decades, several new conventional therapies have been developed that have significantly improved the prognosis of patients with GBM. Nevertheless, most patients have a limited long-term response to these treatments and survive < 1 year. Therefore, innovative anti-cancer drugs that can be rapidly approved for patient use are urgently needed. One way to achieve accelerated approval is drug repositioning, extending the use of existing drugs for new therapeutic purposes, as it takes less time to validate their biological activity as well as their safety in preclinical models. In this review, a comprehensive analysis of the literature search was performed to list drugs with antiviral, antiparasitic, and antidepressant properties that may be effective in GBM and their putative anti-tumor mechanisms in GBM cells.

PMID:36733195 | DOI:10.2174/1871520623666230202163112

J Proteome Res. 2023 Feb 2. doi: 10.1021/acs.jproteome.2c00646. Online ahead of print.

ABSTRACT

Despite recent advancements, the high mortality rate remains a concern in colon cancer (CAC). Identification of therapeutic markers could prove to be a great asset in CAC management. Multiple studies have reported hyperactivation of de novo lipogenesis (DNL), but its association with the pathology is unclear. This study aims to establish the importance as well as the prognostic and therapeutic potential of DNL in CAC. The key lipogenic enzymes fatty acid synthase along with ATP citrate lyase were quantified using an LC-MS/MS-based targeted proteomics approach in the samples along with the matched controls. The potential capacity of the proteins to distinguish between the tumor and controls was demonstrated using random forest-based class prediction analysis using the peptide intensities. Furthermore, in-depth proteomics of DNL inhibition in the CAC cell line revealed the significance of the pathway in proliferation and metastasis. DNL inhibition affected the major signaling pathways, including DNA repair, PI3K-AKT-mTOR pathway, membrane trafficking, proteasome, etc. The study revealed the upregulation of 26S proteasome machinery as a result of the treatment with subsequent induction of apoptosis. Again, in silico molecular docking-based drug repurposing was performed to find potential drug candidates. Furthermore, we have demonstrated that blocking DNL could be explored as a therapeutic option in CAC treatment.

PMID:36731020 | DOI:10.1021/acs.jproteome.2c00646

J Virol. 2023 Feb 2:e0198722. doi: 10.1128/jvi.01987-22. Online ahead of print.

ABSTRACT

Hepatitis A virus (HAV) infection often causes acute hepatitis, which results in a case fatality rate of 0.2% and fulminant hepatitis in 0.5% of cases. However, no specific potent anti-HAV drug is available on the market to date. In the present study, we focused on inhibition of HAV internal ribosomal entry site (IRES)-mediated translation and investigated novel therapeutic drugs through drug repurposing by screening for inhibitors of HAV IRES-mediated translation and cell viability using a reporter assay and cell viability assay, respectively. The initial screening of 1,158 drugs resulted in 77 candidate drugs. Among them, nicotinamide significantly inhibited HAV HA11-1299 genotype IIIA replication in Huh7 cells. This promising drug also inhibited HAV HM175 genotype IB subgenomic replicon and HAV HA11-1299 genotype IIIA replication in a dose-dependent manner. In the present study, we found that nicotinamide inhibited the activation of activator protein 1 (AP-1) and that knockdown of c-Jun, which is one of the components of AP-1, inhibited HAV HM175 genotype IB IRES-mediated translation and HAV HA11-1299 genotype IIIA and HAV HM175 genotype IB replication. Taken together, the results showed that nicotinamide inhibited c-Jun, resulting in the suppression of HAV IRES-mediated translation and HAV replication, and therefore, it could be useful for the treatment of HAV infection. IMPORTANCE Drug screening methods targeting HAV IRES-mediated translation with reporter assays are attractive and useful for drug repurposing. Nicotinamide (vitamin B3, niacin) has been shown to effectively inhibit HAV replication. Transcription complex activator protein 1 (AP-1) plays an important role in the transcriptional regulation of cellular immunity or viral replication. The results of this study provide evidence that AP-1 is involved in HAV replication and plays a role in the HAV life cycle. In addition, nicotinamide was shown to suppress HAV replication partly by inhibiting AP-1 activity and HAV IRES-mediated translation. Nicotinamide may be useful for the control of acute HAV infection by inhibiting cellular AP-1 activity during HAV infection processes.

PMID:36728416 | DOI:10.1128/jvi.01987-22

J Chin Med Assoc. 2023 Jan 9. doi: 10.1097/JCMA.0000000000000869. Online ahead of print.

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). It has brought tremendous challenges to public health and medical systems around the world. The current strategy for drug repurposing has accumulated some evidence on the use of N-acetylcysteine (NAC) in treating patients with COVID-19. However, the evidence remains debated.

METHODS: We performed the systematic review and meta-analysis that complies with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Five databases and reference lists were searched from inception to May 14, 2022. Studies evaluating the efficacy of NAC in treating patients with COVID-19 were regarded as eligible. The review was registered prospectively on PROSPERO (CRD42022332791).

RESULT: Of 778 records identified from the preliminary search, four studies were enrolled in the final qualitative review and quantitative meta-analysis. A total of 355 patients were allocated into the NAC group and the control group. The evaluated outcomes included intubation rate, improvement, duration of intensive unit stay and hospital stay and mortality. The pooled results showed nonsignificant differences in intubation rate (OR, 0.55; 95% CI, 0.16 to 1.89; P=.34; I2=75%), improvement of oxygenation (MD, 80.84; 95% CI, -38.16 to 199.84; p=.18; I2=98%), ICU stay (MD, -0.74; 95 % CI, -3.19 to 1.71; p=.55; I2=95%), hospital stay (MD, -1.05; 95% CI, - 3.02 to 0.92; p=.30; I2=90%) and mortality (OR, 0.58; 95% CI, 0.23 to 1.45; p=.24; I2=54%). Subsequent trial sequential analysis showed conclusive nonsignificant results for mortality, while the TSA for the other outcomes suggested that a larger sample size is essential.

CONCLUSION: The current evidence reveals NAC is not beneficial for treating patients with COVID- 19 with regard to respiratory outcome, mortality, duration of ICU stay and hospital stay.

PMID:36728396 | DOI:10.1097/JCMA.0000000000000869

J Med Signals Sens. 2022 Nov 10;12(4):341-346. doi: 10.4103/jmss.JMSS_66_20. eCollection 2022 Oct-Dec.

ABSTRACT

Ongoing novel coronavirus (COVID-19) with high mortality is an infectious disease in the world which epidemic in 2019 with human-human transmission. According to the literature, S-protein is one of the main proteins of COVID-19 that bind to the human cell receptor angiotensin-converting enzyme 2 (ACE2). In this study, it was attempted to identify the main effective drugs approved that may be repurposed to the binding site of ACE2. High throughput virtual screening based on the docking study was performed to know which one of the small-molecules had a potential interaction with ACE2 structure. Forasmuch as investigating and identifying the best ACE2 inhibitors among more than 3,500 small-molecules is time-consuming, supercomputer was utilized to apply docking-based virtual screening. Outputs of the proposed computational model revealed that vincristine, vinbelastin and bisoctrizole can significantly bind to ACE2 and may interface with its normal activity.

PMID:36726422 | PMC:PMC9885507 | DOI:10.4103/jmss.JMSS_66_20

ERJ Open Res. 2023 Jan 30;9(1):00495-2022. doi: 10.1183/23120541.00495-2022. eCollection 2023 Jan.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is a rare hereditary disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Recent therapies enable effective restoration of CFTR function of the most common F508del CFTR mutation. This shifts the unmet clinical need towards people with rare CFTR mutations such as nonsense mutations, of which G542X and W1282X are most prevalent. CFTR function measurements in patient-derived cell-based assays played a critical role in preclinical drug development for CF and may play an important role to identify new drugs for people with rare CFTR mutations.

METHODS: Here, we miniaturised the previously described forskolin-induced swelling (FIS) assay in intestinal organoids from a 96-well to a 384-well plate screening format. Using this novel assay, we tested CFTR increasing potential of a 1400-compound Food and Drug Administration (FDA)-approved drug library in organoids from donors with W1282X/W1282X CFTR nonsense mutations.

RESULTS: The 384-well FIS assay demonstrated uniformity and robustness based on coefficient of variation and Z'-factor calculations. In the primary screen, CFTR induction was limited overall, yet interestingly, the top five compound combinations that increased CFTR function all contained at least one statin. In the secondary screen, we indeed verified that four out of the five statins (mevastatin, lovastatin, simvastatin and fluvastatin) increased CFTR function when combined with CFTR modulators. Statin-induced CFTR rescue was concentration-dependent and W1282X-specific.

CONCLUSIONS: Future studies should focus on elucidating genotype specificity and mode-of-action of statins in more detail. This study exemplifies proof of principle of large-scale compound screening in a functional assay using patient-derived organoids.

PMID:36726369 | PMC:PMC9885274 | DOI:10.1183/23120541.00495-2022

BMC Anesthesiol. 2023 Feb 1;23(1):39. doi: 10.1186/s12871-023-01986-5.

ABSTRACT

BACKGROUND: The α2 adrenergic receptor agonist dexmedetomidine is an important intravenous sedative with analgesic properties. Currently available dexmedetomidine reversal agents, like the α2-receptor antagonist atipamezole, cause serious adverse effects at the large dosages required for effective reversal; they are not used clinically. Without reversal agents, emergence times from dexmedetomidine sedation are slow. In this study we tested the ability of low-dose atipamezole, in combination with caffeine, to reverse dexmedetomidine sedation. The low dose of atipamezole employed should not be associated with unwanted effects.

METHODS: Two different sedation protocols were employed. In the first protocol, a bolus of dexmedetomidine was rapidly applied and the drug was allowed to equilibrate for 10 min before rats received either saline (as control) or low-dose atipamezole with caffeine. Following this procedure, rats were placed on their backs. Emergence from sedation was the time for rats to recover their righting reflex and stand with 4 paws on the floor. A second sedation protocol simulated a pediatric magnetic resonance imaging (MRI) scan. Adult rats were sedated with dexmedetomidine for one hour followed by 30 min with both dexmedetomidine and propofol. At the end of 90 min, rats received either saline (control) or a combination of low-dose atipamezole, and caffeine. Recovery of the righting reflex was used as a proxy for emergence from sedation.

RESULTS: Emergence from sedation, the time for rats to recover their righting reflex, decreased by ~ 90% when using an atipamezole dose ~ 20 fold lower than manufacturer's recommendation, supplemented with caffeine. Using an atipamezole dose ~ tenfold lower than recommended, with caffeine, emergence times decreased by ~ 97%. A different stimulant, forskolin, when tested, was as effective as caffeine. For the MRI simulation, emergence times were decreased by ~ 93% by low-dose atipamezole with caffeine.

CONCLUSIONS: Low dose atipamezole with caffeine was effective at reversing dexmedetomidine sedation. Emergence was rapid and the rats regained not only their righting reflex but also their balance and their ability to carry out complex behaviors. These findings suggest that the combination of low dose atipamezole with caffeine may permit rapid clinical reversal of dexmedetomidine without unwanted effects.

PMID:36721095 | DOI:10.1186/s12871-023-01986-5

Yakugaku Zasshi. 2023;143(2):127-132. doi: 10.1248/yakushi.22-00156-2.

ABSTRACT

The effects of drugs and other low-molecular-weight compounds are complex and may be unintended by the developer. These compounds and drugs should be avoided if these unintended effects are harmful; however, unintended effects are not always as harmful as suggested by drug repositioning. Therefore, a comprehensive understanding of complex drug actions is essential. Omics data can be regarded as the nonarbitrary transformation of biological information about a sample into comprehensive numerical information comprising multivariate data with a large number of variables. However, the changes are often based on a small number of elements in different dimensions (i.e., latent variables). The omics data of compound-treated samples comprehensively capture the complex effects of compounds, including their unrecognized aspects. Therefore, finding latent variables in these data is expected to contribute to the understanding of multiple effects. In particular, it can be interpreted as decomposing multiple effects into a smaller number of easily understandable effects. Although latent variable models of omics data have been used to understand the mechanisms of diseases, no approach has considered the multiple effects of compounds and their decomposition. Therefore, we propose to decompose and understand the multiple effects of low-molecular-weight compounds without arbitrariness and have been developing analytical methods and verifying their usefulness. In particular, we focused on classical factor analysis among latent variable models and have been examining the biological validity of the estimates obtained under linear assumptions.

PMID:36724926 | DOI:10.1248/yakushi.22-00156-2

Mol Omics. 2023 Jan 27. doi: 10.1039/d2mo00267a. Online ahead of print.

ABSTRACT

The most common treatment strategies for Parkinson's disease (PD) aim to slow down the neurodegeneration process or control the symptoms. In this study, using an in vitro PD model we carried out a transcriptome-based drug target prediction strategy. We identified novel drug target candidates by mapping genes upregulated in 6-OHDA-treated cells on a human protein-protein interaction network. Among the predicted targets, we show that AKR1C3 and CEBPB are promising in validating our bioinformatics approach since their known ligands, rutin and quercetin, respectively, act as neuroprotective drugs that effectively decrease cell death, and restore the expression profiles of key genes upregulated in 6-OHDA-treated cells. We also show that these two genes upregulated in our in vitro PD model are downregulated to basal levels upon drug administration. As a further validation of our methodology, we further confirm that the potential target genes identified with our bioinformatics approach are also upregulated in post-mortem transcriptome samples of PD patients from the literature. Therefore, we propose that this methodology predicts novel drug targets AKR1C3 and CEBPB, which are relevant to future clinical applications as potential drug repurposing targets for PD. Our systems-based computational approach to predict candidate drug targets can be employed in identifying novel drug targets in other diseases without a priori assumption.

PMID:36723117 | DOI:10.1039/d2mo00267a

Indian J Pharmacol. 2022 Nov-Dec;54(6):431-442. doi: 10.4103/ijp.ijp_111_22.

ABSTRACT

INTRODUCTION: Binding of linoleic acid (LA) to the spike trimer stabilizes it in closed conformation hindering its binding to angiotensin-converting enzyme-2, thus decreasing infectivity. In the current study, we tend to repurpose Food and Drug Administration-approved drugs as binder to the LA binding pocket in wild and double mutant spike protein.

MATERIALS AND METHODS: Approved drugs from DrugBank database (n = 2456) were prepared using Ligprep module of Schrodinger. Crystal structure of LA bound to spike trimer was retrieved (PDB: 6ZB4) and prepared using protein preparation wizard and grid was generated. A virtual screening was performed. With the help of molecular dynamics (MD) studies interaction profile of screened drugs were further evaluated. The selected hits were further evaluated for binding to the double mutant form of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

RESULTS AND DISCUSSION: Following virtual screening, a total of 26 molecules were shortlisted, which were further evaluated using 1ns MD simulation study. Four ligands showing better root mean square deviation (RMSD), RMSD to LA with interaction profile similar to LA were further evaluated using 100 ns MD simulation studies. A total of 2 hits were identified, which performed better than LA (selexipag and pralatrexate). Both these ligands were also found to bind to LA binding site of the double mutant form (E484Q and L452R); however, the binding affinity of pralatrexate was found to be better.

CONCLUSION: We have identified 2 ligands (selexipag and pralatrexate) as possible stable binders to the LA binding site in spike trimer (wild and mutant form). Among them, pralatrexate has shown in vitro activity against SARS-CoV-2, validating our study results.

PMID:36722555 | DOI:10.4103/ijp.ijp_111_22

Adv Pharm Bull. 2023 Jan;13(1):113-122. doi: 10.34172/apb.2023.012. Epub 2022 Jan 3.

ABSTRACT

Purpose: Drug repurposing is an approach successfully used for discovery of new therapeutic applications for the existing drugs. The current study was aimed to use the combination of in silico methods to identify FDA-approved drugs with possible S1P1 agonistic activity useful in multiple sclerosis (MS). Methods: For this, a 3D-QSAR model for the known 21 S1P1 agonists were generated based on 3D-QSAR approach and used to predict the possible S1P1 agonistic activity of FDA-approved drugs. Then, the selected compounds were screened by docking into S1P1 and S1P3 receptors to select the S1P1 potent and selective compounds. Further evaluation was carried out by molecular dynamics (MD) simulation studies where the S1P1 binding energies of selected compounds were calculated. Results: The analyses resulted in identification of cobicistat, benzonatate and brigatinib as the selective and potent S1P1 agonists with the binding energies of -85.93, -69.77 and -67.44 kcal. mol-1, calculated using MM-GBSA algorithm based on 50 ns MD simulation trajectories. These values are better than that of siponimod (-59.35 kcal mol-1), an FDA approved S1P1 agonist indicated for MS treatment. Furthermore, similarity network analysis revealed that cobicistat and brigatinib are the most structurally favorable compounds to interact with S1P1. Conclusion: The findings in this study revealed that cobicistat and brigatinib can be evaluated in experimental studies as potential S1P1 agonist candidates useful in the treatment of MS.

PMID:36721815 | PMC:PMC9871275 | DOI:10.34172/apb.2023.012

Brief Bioinform. 2023 Jan 31:bbad032. doi: 10.1093/bib/bbad032. Online ahead of print.

ABSTRACT

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

PMID:36719112 | DOI:10.1093/bib/bbad032

Curr Top Med Chem. 2023 Jan 30. doi: 10.2174/1568026623666230130150029. Online ahead of print.

ABSTRACT

Drug repurposing is a strategy used to develop new treatments based on approved or investigational drugs outside the scope of their original clinical indication. Since this approach benefits from the original toxicity data of the repurposed drugs, the drug-repurposing strategy is time-saving, and inexpensive. It has a higher success rate compared to traditional drug discovery. Several repurposing candidates have been identified in silico screening and in vitro methodologies. One of the best examples is non-steroidal anti-inflammatory drugs [NSAIDs]. Tumor-promoting inflammation is one of the hallmarks of cancer, revealing a connection between inflammatory processes and tumor progression and development. This explains why using NSAIDs in the context of neoplasia has become a topic of interest. Indeed, identifying NSAIDs with antitumor activity has become a promising strategy for finding novel cancer treatment opportunities. Indeed, several commercial anti-inflammatory drugs, including aspirin, ibuprofen, diclofenac, celecoxib, tepoxalin and cyclovalone, naproxen, and indomethacin have presented antitumor activity, and some of them are already in clinical trials for cancer treatment. However, the benefits and complications of using NSAIDs for cancer treatment must be carefully evaluated, particularly for cancer patients with no further therapeutic options available. This review article provides insight into the drug repurposing strategy and describes some of the well-known NSAIDs that have been investigated as repurposed drugs with potential anticancer activity.

PMID:36717997 | DOI:10.2174/1568026623666230130150029

J Periodontol. 2023 Jan 30. doi: 10.1002/JPER.22-0535. Online ahead of print.

ABSTRACT

BACKGROUND: Silibinin has shown various pharmacological effects could be attributed to its antioxidant, anti-inflammatory, and immunoregulatory properties. However, the therapeutic potential of silibinin for periodontitis has not been investigated.

METHODS: The therapeutic effects of silibinin in ligation-induced experimental periodontitis were investigated using biochemical, histological, and immunohistochemical methods. The effects of silibinin on the osteoclastogenesis of RAW264.7 cells were investigated using TRAP staining, qPCR, pit formation, and immunoblotting. Moreover, its effects on inflammatory cytokine production, RANKL expression, and oxidative stress in lipopolysaccharide (LPS)-stimulated human gingival fibroblasts (HGFs) were evaluated using qPCR, and flow cytometry. A coculture system was established to elucidate the effects of silibinin on the crosstalk between LPS-stimulated HGFs and undifferentiated monocytes.

RESULTS: Silibinin significantly reduced the alveolar bone loss, decreased the gingival inflammation and RANKL expression, and decreased the RANKL/OPG ratio in gingival tissues in experimental periodontitis. The in vitro results showed that silibinin inhibited RANKL-induced osteoclast differentiation and function of RAW264.7 cells, suppressed RANKL-induced NFATc1 induction and translocation through the NF-κB and MAPK signaling pathways. Silibinin decreased the inflammatory cytokine level and oxidative stress production in LPS-stimulated HGFs; significantly supressed membrane-bound RANKL expression on LPS-stimulated HGFs; and significantly disrupted TRAP+ cell differentiation in the coculture system.

CONCLUSIONS: Silibinin effectively inhibits inflammation-induced bone loss in experimental periodontitis based on the regulation of stimulated HGFs by inhibiting the expression of inflammatory and osteoclastogenic mediators. Collectively, targeting the inflamed HGF resolution that mediates osteogenesis may employ silibinin as a potential drug repurposing candidate for modulating alveolar bone destruction in periodontitis. This article is protected by copyright. All rights reserved.

PMID:36716169 | DOI:10.1002/JPER.22-0535

Emerg Microbes Infect. 2023 Jan 30:2174777. doi: 10.1080/22221751.2023.2174777. Online ahead of print.

ABSTRACT

Zika virus (ZIKV) infections are typically asymptomatic but cause severe neurological complications (e.g. Guillain-Barré syndrome in adults, and microcephaly in newborns). There are currently no specific therapy or vaccine options available to prevent ZIKV infections. Temporal gene expression profiles of ZIKV-infected human brain microvascular endothelial cells (HBMECs) were used in this study to identify genes essential for viral replication. These genes were then used to identify novel anti-ZIKV agents and validated in publicly available data and functional wet-lab experiments. Here, we found that ZIKV effectively evaded activation of immune response-related genes and completely reprogrammed cellular transcriptional architectures. Knockdown of genes, which gradually upregulated during viral infection but showed distinct expression patterns between ZIKV- and mock infection, discovered novel proviral and antiviral factors. One-third of the 74 drugs found through signature-based drug repositioning and cross-reference with the Drug Gene Interaction Database (DGIdb) were known anti-ZIKV agents. In cellular assays, two promising antiviral candidates (Luminespib/NVP-AUY922, L-161982) were found to reduce viral replication without causing cell toxicity. Overall, our time-series transcriptome-based methods offer a novel and feasible strategy for antiviral drug discovery. Our strategies, which combine conventional and data-driven analysis, can be extended for other pathogens causing pandemics in the future.

PMID:36715162 | DOI:10.1080/22221751.2023.2174777

bioRxiv. 2023 Jan 6:2023.01.05.522939. doi: 10.1101/2023.01.05.522939. Preprint.

ABSTRACT

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degree's predictive performance diminishes when the networks used for training and testing-despite measuring the same biological relationships-were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package ( https://github.com/hetio/xswap/ ).

PMID:36711569 | PMC:PMC9881952 | DOI:10.1101/2023.01.05.522939

bioRxiv. 2023 Jan 7:2023.01.05.522941. doi: 10.1101/2023.01.05.522941. Preprint.

ABSTRACT

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

PMID:36711546 | PMC:PMC9882000 | DOI:10.1101/2023.01.05.522941

Virus Res. 2023 Jan 26:199053. doi: 10.1016/j.virusres.2023.199053. Online ahead of print.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is an acute viral disease with millions of cases worldwide. Although the number of daily new cases and deaths has been dropping, there is still a need for therapeutic alternatives to deal with severe cases. A promising strategy to prospect new therapeutic candidates is to investigate the regulatory mechanisms involved in COVID-19 progression using integrated transcriptomics approaches. In this work, we aimed to identify COVID-19 Master Regulators (MRs) using a series of publicly available gene expression datasets of lung tissue from patients which developed the severe form of the disease. We were able to identify a set of six potential COVID-19 MRs related to its severe form, namely TAL1, TEAD4, EPAS1, ATOH8, ERG, and ARNTL2. In addition, using the Connectivity Map drug repositioning approach, we identified 52 different drugs which could be used to revert the disease signature, thus being candidates for the design of novel clinical treatments. Furthermore, we compared the identified signature and drugs with the ones obtained from the analysis of nasopharyngeal swab samples from infected patients and preclinical cell models. This comparison showed significant similarities between them, although also revealing some limitations on the overlap between clinical and preclinical data in COVID-19, highlighting the need for careful selection of the best model for each disease stage.

PMID:36709793 | DOI:10.1016/j.virusres.2023.199053

J Biomol Struct Dyn. 2023 Jan 29:1-14. doi: 10.1080/07391102.2023.2171135. Online ahead of print.

ABSTRACT

Most of the existing DENV entry inhibitors were discovered through structure-based, high-throughput screening techniques and optimization approaches by aiming β-OG pocket. However, the class of precise chemical scaffolds with superior antiviral activity targeting the early stages of virus infection that is considered to be beneficial in therapeutics and is still in process. In this study, ligand-based pharmacophore modeling using existing DENV entry inhibitors provided two best models, AADRR-2 and AAADR-2 (A- accepter, D- donor, R-ring) to screen public and DrugBank datasets. Further, approximately 36000 molecules were filtered using Zinc13 by employing the ideal validated models. Additionally, using β-OG binding pocket as target site, molecular docking experiments including induced-fit studies were conducted that provided further structurally divergent ligands. Moreover, the refined list of preferential hits were filtered out based on the best fitness score, binding energy and interaction paradigm, among them fused pyrimidine, hydrazone and biphenyl core comprising scaffolds were identified possessing profound interaction profile with key amino acid residues, ALA-50, GLN-200, PHE-193 and PHE-279 in 100 ns MD simulations. Additionally, the search for similar chemical fingerprints from DrugBank library was also carried out and Eltrombopag (Promacta/Revolade® prescribed in thrombocytopenia) was identified as a preferential β-OG pocket binder. The identified pyrazole-based hydrazone class of drug, Eltrombopag is in phase II clinical trials employed to treat dengue-mediated thrombocytopenia.Communicated by Ramaswamy H. Sarma.

PMID:36709443 | DOI:10.1080/07391102.2023.2171135