Mol Biol Cell. 2021 Apr 28:mbcE20080502. doi: 10.1091/mbc.E20-08-0502. Online ahead of print.

ABSTRACT

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin re-organization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin re-organization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.

PMID:33909457 | DOI:10.1091/mbc.E20-08-0502

J Psychopharmacol. 2021 Apr 28:2698811211009798. doi: 10.1177/02698811211009798. Online ahead of print.

NO ABSTRACT

PMID:33908299 | DOI:10.1177/02698811211009798

bioRxiv. 2021 Apr 22:2021.04.20.440626. doi: 10.1101/2021.04.20.440626. Preprint.

ABSTRACT

The SARS-CoV-2 pandemic has caused widespread illness, loss of life, and socioeconomic disruption that is unlikely to resolve until vaccines are widely adopted, and effective therapeutic treatments become established. Here, a well curated and annotated library of 6710 clinical and preclinical molecules, covering diverse chemical scaffolds and known host targets was evaluated for inhibition of SARS-CoV-2 infection in multiple infection models. Multi-concentration, high-content immunocytofluorescence-based screening identified 172 strongly active small molecules, including 52 with submicromolar potencies. The active molecules were extensively triaged by in vitro mechanistic assays, including human primary cell models of infection and the most promising, obatoclax, was tested for in vivo efficacy. Structural and mechanistic classification of compounds revealed known and novel chemotypes and potential host targets involved in each step of the virus replication cycle including BET proteins, microtubule function, mTOR, ER kinases, protein synthesis and ion channel function. In the mouse disease model obatoclax effectively reduced lung virus load by 10-fold. Overall, this work provides an important, publicly accessible, foundation for development of novel treatments for COVID-19, establishes human primary cell-based pharmacological models for evaluation of therapeutics and identifies new insights into SARS-CoV-2 infection mechanisms.

SIGNIFICANCE: A bioinformatically rich library of pharmacologically active small molecules with diverse chemical scaffolds and including known host targets were used to identify hundreds of SARS-CoV-2 replication inhibitors using in vitro, ex vivo, and in vivo models. Extending our previous work, unbiased screening demonstrated a propensity for compounds targeting host proteins that interact with virus proteins. Representatives from multiple chemical classes revealed differences in cell susceptibility, suggesting distinct dependencies on host factors and one, Obatoclax, showed 90% reduction of lung virus loads in the mouse disease model. Our findings and integrated analytical approaches will have important implications for future drug screening and how therapies are developed against SARS-CoV-2 and other viruses.

PMID:33907750 | PMC:PMC8077576 | DOI:10.1101/2021.04.20.440626

Proc Natl Acad Sci U S A. 2021 May 11;118(19):e2025581118. doi: 10.1073/pnas.2025581118.

ABSTRACT

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

PMID:33906951 | DOI:10.1073/pnas.2025581118

Immunotherapy. 2021 Apr 28. doi: 10.2217/imt-2020-0349. Online ahead of print.

ABSTRACT

A dysregulated immune response characterized by the hyperproduction of several pro-inflammatory cytokines (a.k.a. 'cytokine storm') plays a central role in the pathophysiology of severe coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this Perspective article we discuss the evidence for synergistic anti-inflammatory and immunomodulatory properties exerted by vitamin D and dipeptidyl peptidase-4 (DPP-4) inhibitors, the latter being a class of antihyperglycemic agents used for the treatment of Type 2 diabetes, which have also been reported as immunomodulators. Then, we provide the rationale for investigation of vitamin D and DPP-4 inhibitor combination therapy (VIDPP-4i) as an immunomodulation strategy to ratchet down the virulence of SARS-CoV-2, prevent disease progression and modulate the cytokine storm in COVID-19.

PMID:33906375 | DOI:10.2217/imt-2020-0349

BMC Biomed Eng. 2021 Apr 26;3(1):7. doi: 10.1186/s42490-021-00052-w.

ABSTRACT

BACKGROUND: Glioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer.

RESULTS: We use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma.

CONCLUSIONS: Given the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients.

PMID:33902757 | DOI:10.1186/s42490-021-00052-w

Biochim Biophys Acta Rev Cancer. 2021 Apr 23:188548. doi: 10.1016/j.bbcan.2021.188548. Online ahead of print.

ABSTRACT

BACKGROUND: The concurrent growth of large-scale oncology data and the concomitant computational methods with which to analyze and model it has created a promising environment for revolutionizing cancer diagnosis, treatment, prevention, and drug discovery. Computational methods applied to large datasets have accelerated the drug discovery process by reducing bottlenecks and widening the search space beyond what is experimentally tractable. As the research community gains understanding of the myriad genetic underpinnings of cancer via sequencing, imaging, screens, and more that are ingested, transformed, and modeled by top open-source machine learning and artificial intelligence tools readily available, the next big drug candidate might seem merely an "Enter" key away. Of course, the reality is more convoluted, but still promising.

SCOPE OF REVIEW: We present methods to approach the process of building an AI model, with strong emphasis on the aspects of model development we believe to be crucial to success but that are not commonly discussed: diligence in posing questions, identifying suitable datasets and curating them, and collaborating closely with biology and oncology experts while designing and evaluating the model. Digital pathology, Electronic Health Records, and other data types outside of high-throughput molecular data are reviewed well by others and outside of the scope of this review. This review emphasizes the limitations of the datasets, computational methods, and our minds. Datasets can be biased towards areas of research interest, funding, and particular patient populations. Neural networks may learn representations and correlations within the data that are grounded not in biological phenomena, but statistical anomalies erroneously extracted from the training data. Researchers may mis-interpret or over-interpret the output, or design and evaluate the training process such that the resultant model generalizes poorly. Fortunately, awareness of the strengths and limitations of applying data analytics and AI to drug discovery enables us to leverage them carefully and insightfully while maximizing their utility. These applications when performed in close collaboration with domain experts, together with continuous critical evaluation, generation of new data to minimize known blind spots as they are found, and rigorous experimental validation, increases the success rate of the study. We will discuss applications including AI-assisted target identification, drug repurposing, patient stratification, and gene prioritization.

MAJOR CONCLUSIONS: Data analytics and AI have demonstrated capabilities to revolutionize cancer research, prevention, and treatment by maximizing our understanding and use of the expanding panoply of experimental data. However, to separate promise from true utility, computational tools must be carefully designed, critically evaluated, and constantly improved. Once that is achieved, a human-computer hybrid discovery process will outperform one driven by each alone.

GENERAL SIGNIFICANCE: This review highlights the challenges and promise of synergizing predictive AI models with human expertise towards greater understanding of cancer.

PMID:33901609 | DOI:10.1016/j.bbcan.2021.188548

J Chem Inf Model. 2021 Apr 26. doi: 10.1021/acs.jcim.1c00009. Online ahead of print.

ABSTRACT

Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug-pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug-pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug-pathway associations based on substructure-drug-pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug-pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug-pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.

PMID:33900090 | DOI:10.1021/acs.jcim.1c00009

Expert Opin Investig Drugs. 2021 Apr 26:1-7. doi: 10.1080/13543784.2021.1916913. Online ahead of print.

ABSTRACT

Introduction: Dementia with Lewy bodies (DLB) is characterized by the toxic accumulation of α-synuclein protein inside neural cells; this results in neurodegeneration which is clinically accompanied by behavioral and psychological changes. DLB shares features with Parkinson's disease (PD) and Parkinson's disease dementia (PDD), but also overlaps neurochemically and pathologically with Alzheimer's disease. Symptomatic treatments for LBD differ in their effectiveness while disease-modifying and curative approaches are much needed.Areas covered: We explore emerging therapeutics for DLB through the lens of repurposing approved drugs and survey their potential for disease modifying actions in DLB. Given the complexity of DLB with multiple pathologies, potential therapeutic targets that could affect Lewy body pathology, or metabolism or neurotransmitters or immunomodulation were surveyed. We queried PubMed and ClinicalTrials.gov searches 2017-2020.Expert opinion: DLB is not simply aredux ofAD or PD; hence, treatments should not be exclusively duplicative ofAD or PD directed treatments. This opens amyriad of possibilities for therapeutic approaches that are disease specific or repurposed.

PMID:33899637 | DOI:10.1080/13543784.2021.1916913

SN Compr Clin Med. 2021 Apr 21:1-18. doi: 10.1007/s42399-021-00874-8. Online ahead of print.

ABSTRACT

This review summarizes published findings of the beneficial and harmful effects on the heart, lungs, immune system, kidney, liver, and central nervous system of 47 drugs that have been proposed to treat COVID-19. Many of the repurposed drugs were chosen for their benefits to the pulmonary system, as well as immunosuppressive and anti-inflammatory effects. However, these drugs have mixed effects on the heart, liver, kidney, and central nervous system. Drug treatments are critical in the fight against COVID-19, along with vaccines and public health protocols. Drug treatments are particularly needed as variants of the SARS-Cov-2 virus emerge with some mutations that could diminish the efficacy of the vaccines. Patients with comorbidities are more likely to require hospitalization and greater interventions. The combination of treating severe COVID-19 symptoms in the presence of comorbidities underscores the importance of understanding the effects of potential COVID-19 treatments on other organs.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42399-021-00874-8.

PMID:33898925 | PMC:PMC8057921 | DOI:10.1007/s42399-021-00874-8

Future Med Chem. 2021 Apr 26. doi: 10.4155/fmc-2020-0311. Online ahead of print.

ABSTRACT

Background: Praziquantel is the only drug available to treat schistosomiasis, and there is an urgent demand for new anthelmintic agents. Methodology & results: We conducted in-depth in vitro and in vivo studies and report a target fishing investigation. In vitro, tamoxifen was active against adult and immature worms at low concentrations (<5 μM). Tamoxifen at a single dose (400 mg/kg) or once daily for five consecutive days (100 mg/kg/day) in mice harboring either adult (patent infection) or juvenile (prepatent infection) significantly reduced worm burden (30-70%) and egg production (70-90%). Target fishing studies revealed propionyl-CoA carboxylase as a potential target for tamoxifen in Schistosoma mansoni and glucose uptake by S. mansoni was also significantly reduced. Conclusion: Our results provide news evidence of antiparasitic effect of tamoxifen and reveal propionyl-CoA carboxylase as a potential target.

PMID:33896196 | DOI:10.4155/fmc-2020-0311

Signal Transduct Target Ther. 2021 Apr 24;6(1):165. doi: 10.1038/s41392-021-00568-6.

ABSTRACT

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

PMID:33895786 | DOI:10.1038/s41392-021-00568-6

Trends Microbiol. 2021 Apr 22:S0966-842X(21)00093-7. doi: 10.1016/j.tim.2021.03.017. Online ahead of print.

ABSTRACT

The word 'antibiotics' is an historical, but imprecise, term. Today, 'antibiotics' are also used for other indications and 'non-antibiotics' are repurposed for infectious diseases. This situation calls for a revision of antipathogenic drug terminology. The use of correct terms will facilitate rational antipathogenic treatment and understanding of drug repurposing.

PMID:33895061 | DOI:10.1016/j.tim.2021.03.017

Cancer Biol Med. 2021 Apr 24:j.issn.2095-3941.2020.0218. doi: 10.20892/j.issn.2095-3941.2020.0218. Online ahead of print.

ABSTRACT

OBJECTIVE: Drug repurposing, the application of existing therapeutics to new indications, holds promise in achieving rapid clinical effects at a much lower cost than that of de novo drug development. The aim of our study was to perform a more comprehensive drug repurposing prediction of diseases, particularly cancers.

METHODS: Here, by targeting 4,096 human diseases, including 384 cancers, we propose a greedy computational model based on a heterogeneous multilayer network for the repurposing of 1,419 existing drugs in DrugBank. We performed additional experimental validation for the dominant repurposed drugs in cancer.

RESULTS: The overall performance of the model was well supported by cross-validation and literature mining. Focusing on the top-ranked repurposed drugs in cancers, we verified the anticancer effects of 5 repurposed drugs widely used clinically in drug sensitivity experiments. Because of the distinctive antitumor effects of nifedipine (an antihypertensive agent) and nortriptyline (an antidepressant drug) in prostate cancer, we further explored their underlying mechanisms by using quantitative proteomics. Our analysis revealed that both nifedipine and nortriptyline affected the cancer-related pathways of DNA replication, the cell cycle, and RNA transport. Moreover, in vivo experiments demonstrated that nifedipine and nortriptyline significantly inhibited the growth of prostate tumors in a xenograft model.

CONCLUSIONS: Our predicted results, which have been released in a public database named The Predictive Database for Drug Repurposing (PAD), provide an informative resource for discovering and ranking drugs that may potentially be repurposed for cancer treatment and determining new therapeutic effects of existing drugs.

PMID:33893730 | DOI:10.20892/j.issn.2095-3941.2020.0218

J Mol Graph Model. 2021 Apr 13;106:107916. doi: 10.1016/j.jmgm.2021.107916. Online ahead of print.

ABSTRACT

The life challenging COVID-19 disease caused by the SARS-CoV-2 virus has greatly impacted smooth survival worldwide since its discovery in December 2019. Currently, it is one of the major threats to humanity. Moreover, any specific drug or vaccine unavailability against COVID-19 forces to discover a new drug on an urgent basis. Viral cycle inhibition could be one possible way to prevent the further genesis of this viral disease, which can be contributed by drug repurposing techniques or screening of small bioactive natural molecules against already validated targets of COVID-19. The main protease (Mpro) responsible for producing functional proteins from polyprotein is an important key step for SARS-CoV-2 virion replication. Natural product or herbal based formulations are an important platform for potential therapeutics and lead compounds in the drug discovery process. Therefore, here we have screened >53,500 bioactive natural molecules from six different natural product databases against Mpro (PDB ID: 6LU7) of COVID-19 through computational study. Further, the top three molecules were subjected to pharmacokinetics evaluation, which is an important factor that reduces the drug failure rate. Moreover, the top three screened molecules (C00014803, C00006660, ANLT0001) were further validated by a molecular dynamics study under a condition similar to the physiological one. Relative binding energy analysis of three lead molecules indicated that C00014803 possess highest binding affinity among all three hits. These extensive studies can be a significant foundation for developing a therapeutic agent against COVID-19 through vet lab studies.

PMID:33892297 | DOI:10.1016/j.jmgm.2021.107916

Eur J Med Chem. 2021 Apr 14;219:113440. doi: 10.1016/j.ejmech.2021.113440. Online ahead of print.

ABSTRACT

Breast cancer is the most dangerous, among all malignant tumors that threaten women's lives and health. Surgical resection can effectively prolong the survival time of patients with early breast cancer. Insulin-like growth factor type 1 receptor (IGF1R) is a member of the large family of receptor tyrosine kinases, and it's significantly overexpressed in breast cancer cells, which make them ideal biomarkers for the diagnosis and surgery navigation of breast cancer. Herein, we developed a series of IGF1R-targeted probes (YQ-L) for fluorescent imaging in breast cancer based on the strategy of drug repositioning. YQ-L exhibited specific IGF1R binding both in vitro and in vivo, especially probe 5d exhibited higher tumor uptake with a high tumor/normal ratio in the MCF-7 tumor bearing mouse. The maximum T/N ratio of probe 5d was 4.9, which was about 3 times that of indocyanine green (ICG). Meanwhile, probe 5d displayed more favorable in vivo pharmacokinetic properties than that of ICG with less hepatic and intestinal uptake. Convenient preparation, excellent IGF1R specificity in breast cancer, rapid clearance from normal organs and good biosafety profiles of probe 5d warrant further investigations for clinical translation in detection and surgery navigation of breast cancer.

PMID:33892274 | DOI:10.1016/j.ejmech.2021.113440

J Am Coll Cardiol. 2021 Apr 27;77(16):2040-2052. doi: 10.1016/j.jacc.2021.02.056.

ABSTRACT

The National Heart, Lung, and Blood Institute and the Cardiovascular Medical Research and Education Fund held a workshop on the application of pulmonary vascular disease omics data to the understanding, prevention, and treatment of pulmonary vascular disease. Experts in pulmonary vascular disease, omics, and data analytics met to identify knowledge gaps and formulate ideas for future research priorities in pulmonary vascular disease in line with National Heart, Lung, and Blood Institute Strategic Vision goals. The group identified opportunities to develop analytic approaches to multiomic datasets, to identify molecular pathways in pulmonary vascular disease pathobiology, and to link novel phenotypes to meaningful clinical outcomes. The committee suggested support for interdisciplinary research teams to develop and validate analytic methods, a national effort to coordinate biosamples and data, a consortium of preclinical investigators to expedite target evaluation and drug development, longitudinal assessment of molecular biomarkers in clinical trials, and a task force to develop a master clinical trials protocol for pulmonary vascular disease.

PMID:33888254 | DOI:10.1016/j.jacc.2021.02.056

PLoS One. 2021 Apr 22;16(4):e0250019. doi: 10.1371/journal.pone.0250019. eCollection 2021.

ABSTRACT

SARS-CoV-2 has caused a global pandemic, and has taken over 1.7 million lives as of mid-December, 2020. Although great progress has been made in the development of effective countermeasures, with several pharmaceutical companies approved or poised to deliver vaccines to market, there is still an unmet need of essential antiviral drugs with therapeutic impact for the treatment of moderate-to-severe COVID-19. Towards this goal, a high-throughput assay was used to screen SARS-CoV-2 nsp15 uracil-dependent endonuclease (endoU) function against 13 thousand compounds from drug and lead repurposing compound libraries. While over 80% of initial hit compounds were pan-assay inhibitory compounds, three hits were confirmed as nsp15 endoU inhibitors in the 1-20 μM range in vitro. Furthermore, Exebryl-1, a ß-amyloid anti-aggregation molecule for Alzheimer's therapy, was shown to have antiviral activity between 10 to 66 μM, in Vero 76, Caco-2, and Calu-3 cells. Although the inhibitory concentrations determined for Exebryl-1 exceed those recommended for therapeutic intervention, our findings show great promise for further optimization of Exebryl-1 as an nsp15 endoU inhibitor and as a SARS-CoV-2 antiviral.

PMID:33886614 | DOI:10.1371/journal.pone.0250019

Acc Chem Res. 2021 Apr 22. doi: 10.1021/acs.accounts.0c00878. Online ahead of print.

ABSTRACT

Conspectus"Drug resistance is an unavoidable consequence of the use of drugs; however, the emergence of multi-drug resistance can be managed by accurate diagnosis and tailor-made regimens."Antimicrobial resistance (AMR), is one of the most paramount health perils that has emerged in the 21st century. The global increase in drug-resistant strains of various bacterial pathogens prompted the World Health Organization (WHO) to develop a priority list of AMR pathogens. Mycobacterium tuberculosis (Mtb), an acid-fast bacillus that causes tuberculosis (TB), merits being one of the highest priority pathogens on this list since drug-resistant TB (DR-TB) accounts for ∼29% of deaths attributable to AMR. In recent years, funded collaborative efforts of researchers from academia, not-for-profit virtual R&D organizations and industry have resulted in the continuous growth of the TB drug discovery and development pipeline. This has so far led to the accelerated regulatory approval of bedaquiline and delamanid for the treatment of DR-TB. However, despite the availability of drug regimes, the current cure rate for multi-drug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) treatment regimens is 50% and 30%, respectively. It is to be noted that these regimens are administered over a long duration and have a serious side effect profile. Coupled with poor patient adherence, this has led to further acquisition of drug resistance and treatment failure. There is therefore an urgent need to develop new TB drugs with novel mechanism of actions (MoAs) and associated regimens.This Account recapitulates drug resistance in TB, existing challenges in addressing DR-TB, new drugs and regimens in development, and potential ways to treat DR-TB. We highlight our research aimed at identifying novel small molecule leads and associated targets against TB toward contributing to the global TB drug discovery and development pipeline. Our work mainly involves screening of various small molecule chemical libraries in phenotypic whole-cell based assays to identify hits for medicinal chemistry optimization, with attendant deconvolution of the MoA. We discuss the identification of small molecule chemotypes active against Mtb and subsequent structure-activity relationships (SAR) and MoA deconvolution studies. This is followed by a discussion on a chemical series identified by whole-cell cross-screening against Mtb, for which MoA deconvolution studies revealed a pathway that explained the lack of in vivo efficacy in a mouse model of TB and reiterated the importance of selecting an appropriate growth medium during phenotypic screening. We also discuss our efforts on drug repositioning toward addressing DR-TB. In the concluding section, we preview some promising future directions and the challenges inherent in advancing the drug pipeline to address DR-TB.

PMID:33886255 | DOI:10.1021/acs.accounts.0c00878

Nucleic Acids Res. 2021 Apr 22:gkab278. doi: 10.1093/nar/gkab278. Online ahead of print.

ABSTRACT

Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3Dweb, the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3Dweb, users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3Dweb supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3Dweb using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3Dweb is available at http://bib.fleming.gr:3838/Arena3D or http://bib.fleming.gr/Arena3D.

PMID:33885790 | DOI:10.1093/nar/gkab278