DNA Repair (Amst). 2021 Jul 16;106:103180. doi: 10.1016/j.dnarep.2021.103180. Online ahead of print.

ABSTRACT

Since the early stages of the pandemic, hydroxychloroquine (HCQ), a widely used drug with good safety profile in clinic, has come to the forefront of research on drug repurposing for COVID-19 treatment/prevention. Despite the decades-long use of HCQ in the treatment of diseases, such as malaria and autoimmune disorders, the exact mechanisms of action of this drug are only beginning to be understood. To date, no data are available on the genotoxic potential of HCQ in vitro or in vivo. The present study is the first investigation of the DNA damaging- and mutagenic effects of HCQ in mammalian cells in vitro, at concentrations that are comparable to clinically achievable doses in patient populations. We demonstrate significant induction of a representative oxidative DNA damage (8-oxodG) in primary mouse embryonic fibroblasts (MEFs) treated with HCQ at 5 and 25 μM concentrations (P = 0.020 and P = 0.029, respectively), as determined by enzyme-linked immunosorbent assay. Furthermore, we show significant mutagenicity of HCQ, manifest as 2.2- and 1.8-fold increases in relative cII mutant frequency in primary and spontaneously immortalized Big Blue® MEFs, respectively, treated with 25 μM dose of this drug (P = 0.005 and P = 0.012, respectively). The observed genotoxic effects of HCQ in vitro, achievable at clinically relevant doses, are novel and important, and may have significant implications for safety monitoring in patient populations. Given the substantial number of the world's population receiving HCQ for the treatment of various chronic diseases or in the context of clinical trials for COVID-19, our findings warrant further investigations into the biological consequences of therapeutic/preventive use of this drug.

PMID:34298488 | DOI:10.1016/j.dnarep.2021.103180

OMICS. 2021 Jul 23. doi: 10.1089/omi.2021.0085. Online ahead of print.

ABSTRACT

Esophageal squamous cell carcinoma (ESCC) is among the most dangerous cancers with high mortality and lack of robust diagnostics and personalized/precision therapeutics. To achieve a systems-level understanding of tumorigenesis, unraveling of variations in the protein interactome and determination of key proteins exhibiting significant alterations in their interaction patterns during tumorigenesis are crucial. To this end, we have described differential protein-protein interactions and differentially interacting proteins (DIPs) in ESCC by utilizing the human protein interactome and transcriptome. Furthermore, DIP-centered modules were analyzed according to their potential in elucidation of disease mechanisms and improvement of efficient diagnostic, prognostic, and treatment strategies. Seven modules were presented as potential diagnostic, and 16 modules were presented as potential prognostic biomarker candidates. Importantly, our findings also suggest that 30 out of the 53 repurposed drugs were noncancer drugs, which could be used in the treatment of ESCC. Interestingly, 25 of these, proposed as novel drug candidates here, have not been previously associated in a context of esophageal cancer. In this context, risperidone and clozapine were validated for their growth inhibitory potential in three ESCC lines. Our findings offer a high potential for the development of innovative diagnostic, prognostic, and therapeutic strategies for further experimental studies in line with predictive diagnostics, targeted prevention, and personalization of medical services in ESCC specifically, and personalized cancer care broadly.

PMID:34297901 | DOI:10.1089/omi.2021.0085

J Chem Inf Model. 2021 Jul 23. doi: 10.1021/acs.jcim.1c00642. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has motivated researchers all over the world in trying to find effective drugs and therapeutics for treating this disease. To save time, much effort has focused on repurposing drugs known for treating other diseases than COVID-19. To support these drug repurposing efforts, we built the CAS Biomedical Knowledge Graph and identified 1350 small molecules as potentially repurposable drugs that target host proteins and disease processes involved in COVID-19. A computer algorithm-driven drug-ranking method was developed to prioritize those identified small molecules. The top 50 molecules were analyzed according to their molecular functions and included 11 drugs in clinical trials for treating COVID-19 and new candidates that may be of interest for clinical investigation. The CAS Biomedical Knowledge Graph provides researchers an opportunity to accelerate innovation and streamline the investigative process not just for COVID-19 but also in many other diseases.

PMID:34297570 | DOI:10.1021/acs.jcim.1c00642

Mol Divers. 2021 Jul 23. doi: 10.1007/s11030-021-10273-9. Online ahead of print.

ABSTRACT

Identifying drug-target protein association pairs is a prerequisite and a crucial task in drug discovery and development. Numerous computational models, based on different assumptions and algorithms, have been proposed as an alternative to the laborious, costly, and time-consuming traditional wet-lab methods. Most proposed methods focus on separated drug and target descriptors, calculated, respectively, from chemical structures and protein sequences, and fail to introduce and extract features where the interaction information is embedded. In this paper, we propose a new three-step method based on matrix factorisation and generative adversarial network (GAN) for drug-target interaction prediction. Firstly, the matrix factorisation technique is used to capture and extract the joint interaction feature, for both drugs and targets, from the drug-target interaction matrix. Then, a GAN is introduced for data augmentation. It generates a fake positive sample similar to the real positive sample (known interactions) in order to balance the samples, allow the exploitation of the entire negative sample, and increase the data size for an accurate prediction. Finally, a fully connected four-layer neural network is built for classification. Experimental results illustrate a higher prediction performance of the proposed method compared to shallow classifiers and to state-of-the-art methods with an accuracy higher than 97%. Moreover, the data generation effect is confirmed by evaluating the proposed method with and without the generation step. These results demonstrated the efficiency of the latent interaction features and data generation on predicting new drugs or repurposing existing drugs. Overview of the WGANMF-DTI workflow for the Drug-Target Interaction Prediction task.

PMID:34297278 | DOI:10.1007/s11030-021-10273-9

J Control Release. 2021 Jul 19:S0168-3659(21)00371-0. doi: 10.1016/j.jconrel.2021.07.028. Online ahead of print.

ABSTRACT

The coronavirus disease-19 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has taken the world by surprise. To date, a worldwide approved treatment remains lacking and hence in the context of rapid viral spread and the growing need for rapid action, drug repurposing has emerged as one of the frontline strategies in the battle against SARS-CoV2. Repurposed drugs currently being evaluated against COVID-19 either tackle the replication and spread of SARS-CoV2 or they aim at controlling hyper-inflammation and the rampaged immune response in severe disease. In both cases, the target for such drugs resides in the lungs, at least during the period where treatment could still provide substantial clinical benefit to the patient. Yet, most of these drugs are administered systemically, questioning the percentage of administered drug that actually reaches the lung and as a consequence, the distribution of the remainder of the dose to off target sites. Inhalation therapy should allow higher concentrations of the drug in the lungs and lower concentrations systemically, hence providing a stronger, more localized action, with reduced adverse effects. Therefore, the nano-reformulation of the repurposed drugs for inhalation is a promising approach for targeted drug delivery to lungs. In this review, we critically analyze, what nanomedicine could and ought to do in the battle against SARS-CoV2. We start by a brief description of SARS-CoV2 structure and pathogenicity and move on to discuss the current limitations of repurposed antiviral and immune-modulating drugs that are being clinically investigated against COVID-19. This account focuses on how nanomedicine could address limitations of current therapeutics, enhancing the efficacy, specificity and safety of such drugs. With the appearance of new variants of SARS-CoV2 and the potential implication on the efficacy of vaccines and diagnostics, the presence of an effective therapeutic solution is inevitable and could be potentially achieved via nano-reformulation. The presence of an inhaled nano-platform capable of delivering antiviral or immunomodulatory drugs should be available as part of the repertoire in the fight against current and future outbreaks.

PMID:34293319 | DOI:10.1016/j.jconrel.2021.07.028

Expert Opin Investig Drugs. 2021 Jul 22:1-22. doi: 10.1080/13543784.2021.1955102. Online ahead of print.

ABSTRACT

INTRODUCTION: Cholangiocarcinoma (CCA) is a malignant tumor of bile duct epithelium with the highest incidence found in Thailand. Some patients are considered suitable for adjuvant therapy and surgical resection is currently the curative treatment for CCA patients. Tumor recurrence is still a hurdle after treatment; hence, finding novel therapeutic strategies to combat CCA is necessary for improving outcome for patients.

AREAS COVERED: We discuss targeted therapies and other novel treatment approaches which include protein kinase inhibitors, natural products, amino acid transporter-based inhibitors, immunotherapy, and drug repurposing. We also examine the challenges of tumor heterogeneity, cancer stem cells (CSCs), the tumor microenvironment, exosomes, multiomics studies, and the potential of precision medicine.

EXPERT OPINION: Because CCA is difficult to diagnose at the early stage, the traditional treatment approaches are not effective for many patients and most tumors recur. Consequently, researchers are exploring multi-aspect molecular carcinogenesis to uncover molecular targets for further development of novel targeted drugs.

PMID:34292795 | DOI:10.1080/13543784.2021.1955102

Am J Clin Dermatol. 2021 Jul 22. doi: 10.1007/s40257-021-00626-3. Online ahead of print.

ABSTRACT

Epidermolysis bullosa (EB) is a heterogeneous group of rare inherited blistering skin disorders characterized by skin fragility following minor trauma, usually present since birth. EB can be categorized into four classical subtypes, EB simplex, junctional EB, dystrophic EB and Kindler EB, distinguished on clinical features, plane of blister formation in the skin, and molecular pathology. Treatment for EB is mostly supportive, focusing on wound care and patient symptoms such as itch or pain. However, therapeutic advances have also been made in targeting the primary genetic abnormalities as well as the secondary inflammatory footprint of EB. Pre-clinical or clinical testing of gene therapies (gene replacement, gene editing, RNA-based therapy, natural gene therapy), cell-based therapies (fibroblasts, bone marrow transplantation, mesenchymal stromal cells, induced pluripotential stem cells), recombinant protein therapies, and small molecule and drug repurposing approaches, have generated new hope for better patient care. In this article, we review advances in translational research that are impacting on the quality of life for people living with different forms of EB and which offer hope for improved clinical management.

PMID:34292508 | DOI:10.1007/s40257-021-00626-3

Eur J Pharm Sci. 2021 Jul 18:105924. doi: 10.1016/j.ejps.2021.105924. Online ahead of print.

ABSTRACT

The delivery of therapeutic drugs through the skin is a promising alternative to oral or parenteral delivery routes because dermal drug delivery systems (D3S) offer unique advantages such as controlled drug release over sustained periods and a significant reduction in first-pass effects, thus reducing the required dosing frequency and level of patient noncompliance. Furthermore, D3S find applications in multiple therapeutic areas, including drug repurposing. This article presents an integrated biophysical model of dermal absorption for simulating the permeation and absorption of compounds delivered transdermally. The biophysical model is physiologically/biologically inspired and combines a holistic model of healthy skin with whole-body physiology-based pharmacokinetics through dermis microcirculation. The model also includes the effects of chemical penetration enhancers and hair follicles on transdermal transport. The model-predicted permeation and pharmacokinetics of select compounds were validated using in vivo data reported in the literature. We conjecture that the integrated model can be used to gather insights into the permeation and systemic absorption of transdermal formulations (including cosmetic products) released from novel depots and optimize delivery systems. Furthermore, the model can be adapted to diseased skin with parametrization and structural adjustments specific to skin diseases.

PMID:34289340 | DOI:10.1016/j.ejps.2021.105924

STAR Protoc. 2021 Jul 7;2(3):100653. doi: 10.1016/j.xpro.2021.100653. eCollection 2021 Sep 17.

ABSTRACT

Drug repositioning represents a cost- and time-efficient strategy for drug development. Artificial intelligence-based algorithms have been applied in drug repositioning by predicting drug-target interactions in an efficient and high throughput manner. Here, we present a workflow of in silico drug repositioning for host-based antivirals using specially defined targets, a refined list of drug candidates, and an easily implemented computational framework. The workflow described here can also apply to more general purposes, especially when given a user-defined druggable target gene set. For complete details on the use and execution of this protocol, please refer to Li et al. (2021).

PMID:34286288 | PMC:PMC8273420 | DOI:10.1016/j.xpro.2021.100653

Comput Struct Biotechnol J. 2021 Jun 10;19:3674-3681. doi: 10.1016/j.csbj.2021.06.013. eCollection 2021.

ABSTRACT

Mutations in leucine-rich repeat kinase 2 (LRRK2) are a frequent cause of autosomal dominant Parkinson's disease (PD) and have been associated with familial and sporadic PD. Reducing the kinase activity of LRRK2 is a promising therapeutic strategy since pathogenic mutations increase the kinase activity. Several small-molecule LRRK2 inhibitors are currently under investigation for the treatment of PD. However, drug discovery and development are always accompanied by high costs and a risk of late failure. The use of already approved drugs for a new indication, which is known as drug repositioning, can reduce the cost and risk. In this study, we applied a structure-based drug repositioning approach to identify new LRRK2 inhibitors that are already approved for a different indication. In a large-scale structure-based screening, we compared the protein-ligand interaction patterns of known LRRK2 inhibitors with protein-ligand complexes in the PDB. The screening yielded 6 drug repositioning candidates. Two of these candidates, Sunitinib and Crizotinib, demonstrated an inhibition potency (IC50) and binding affinity (Kd) in the nanomolar to micromolar range. While Sunitinib has already been known to inhibit LRRK2, Crizotinib is a novel LRRK2 binder. Our results underscore the potential of structure-based methods for drug discovery and development. In light of the recent breakthroughs in cryo-electron microscopy and structure prediction, we believe that structure-based approaches like ours will grow in importance.

PMID:34285770 | PMC:PMC8258795 | DOI:10.1016/j.csbj.2021.06.013

Mol Cell Neurosci. 2021 Jul 17:103656. doi: 10.1016/j.mcn.2021.103656. Online ahead of print.

ABSTRACT

Multiple sclerosis (MS) is a neuroinflammatory disorder leading to chronic disability. Brain lesions in MS commonly arise in normal-appearing white matter (NAWM). Genome-wide association studies (GWAS) have identified genetic variants associated with MS. Transcriptome alterations have been observed in case-control studies of NAWM. We developed a Cross-Dataset Evaluation (CDE) function for our network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS). We applied CDE to integrate publicly available MS GWAS summary statistics of 41,505 cases and controls with collectively 38 NAWM expression samples, using the human protein interactome as the reference network, to investigate biological underpinnings of MS etiology. We validated the resulting modules with colocalization of GWAS and expression quantitative trait loci (eQTL) signals, using GTEx Consortium expression data for MS-relevant tissues: 14 brain tissues and 4 immune-related tissues. Other network assessments included a drug target query and functional gene set enrichment analysis. CDE prioritized a MS NAWM network containing 55 unique genes. The gene list was enriched (p-value = 2.34 × 10-7) with GWAS-eQTL colocalized genes: CDK4, IFITM3, MAPK1, MAPK3, METTL12B and PIK3R2. The resultant network also included drug signatures of FDA-approved medications. Gene set enrichment analysis revealed the top functional term "intracellular transport of virus", among other viral pathways. We prioritize critical genes from the resultant network: CDK4, IFITM3, MAPK1, MAPK3, METTL12B and PIK3R2. Enriched drug signatures suggest potential drug targets and drug repositioning strategies for MS. Finally, we propose mechanisms of potential MS viral onset, based on prioritized gene set and functional enrichment analysis.

PMID:34284104 | DOI:10.1016/j.mcn.2021.103656

Mol Divers. 2021 Jul 19. doi: 10.1007/s11030-021-10274-8. Online ahead of print.

ABSTRACT

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

PMID:34282519 | DOI:10.1007/s11030-021-10274-8

Int J Mol Sci. 2021 Jul 2;22(13):7164. doi: 10.3390/ijms22137164.

ABSTRACT

Recent reports suggest a link between positive regulation of the Hippo pathway with bipolar disorder (BD), and the Hippo pathway is known to interact with multiple other signaling pathways previously associated with BD and other psychiatric disorders. In this study, neuronal-like NT2 cells were treated with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), valproate (0.5 mM), or vehicle control for 24 h. Genome-wide mRNA expression was quantified and analyzed using gene set enrichment analysis (GSEA), with genes belonging to Hippo, Wnt, Notch, TGF- β, and Hedgehog retrieved from the KEGG database. Five of the eight drugs downregulated the genes of the Hippo pathway and modulated several genes involved in the interacting pathways. We speculate that the regulation of these genes, especially by aripiprazole, clozapine, and quetiapine, results in a reduction of MAPK and NFκB pro-inflammatory signaling through modulation of Hippo, Wnt, and TGF-β pathways. We also employed connectivity map analysis to identify compounds that act on these pathways in a similar manner to the known psychiatric drugs. Thirty-six compounds were identified. The presence of antidepressants and antipsychotics validates our approach and reveals possible new targets for drug repurposing.

PMID:34281223 | DOI:10.3390/ijms22137164

J Interferon Cytokine Res. 2021 Jul;41(7):244-257. doi: 10.1089/jir.2020.0156.

ABSTRACT

Severe Acute Respiratory Syndrome-Coronavirus (SARS-CoV-2), which initiated as an endemic from China, converted into a pandemic disease worldwide within a couple of months' time. This has led researchers from all over the world to come together to find and develop possible curative or preventive strategies, including vaccine development, drug repurposing, plasma therapy, drug discovery, and cytokine-based therapies. Herein, we are providing, a summarized overview of immunopathology of the SARS-CoV-2 along with various therapeutic strategies undertaken to COVID-19 with a vision for their possible outcome. High levels of proinflammatory cytokines such as interleukin (IL)-7, G-CSF, IP-10, TNF-α, monocyte chemoattractant protein-1 (MCP-1), and IL-2 in severe cases of COVID-19 have been observed. Immune responses play significant roles in the determination of SARS-CoV-2 pathogenesis. Thus, exploring the underlying mechanism of the immune system response to SARS-CoV-2 infection would help in the prediction of disease course and selection of intensive care and therapeutic strategy. As an effort toward developing possible therapeutics for COVID-19, we highlighted different types of vaccines, which are under clinical trials, and also discussed the impact of genome variability on efficacy of vaccine under development.

PMID:34280026 | DOI:10.1089/jir.2020.0156

Ann Neurol. 2021 Jul 19. doi: 10.1002/ana.26169. Online ahead of print.

ABSTRACT

OBJECTIVE: To identify disease relevant genes and explore underlying immunological mechanisms that contribute to early and late onset forms of myasthenia gravis.

METHODS: We used a novel genomic methodology to integrate genome wide association study (GWAS) findings in myasthenia gravis with cell-type specific information such as gene expression patterns and promotor-enhancer interactions, in order to identify disease relevant genes. Subsequently, we conducted additional genomic investigations including an expression quantitative analysis of 313 healthy people to provide mechanistic insights.

RESULTS: We identified genes that were specifically linked to early onset myasthenia gravis based on GWAS associated risk variants including TNIP1, ORMDL3, GSDMB and TRAF3. We showed that regulators of toll-like receptor 4 signalling were enriched only among the early onset disease genes (fold enrichment = 3.85, p = 6.4 x 10-3 ). In contrast, T-cell regulators CD28 and CTLA4 were exclusively linked to late onset disease. We identified two causal genetic variants (rs231770 and rs231735; posterior probability= 0.98 and 0.91) near the CTLA4 gene. Subsequently, we demonstrated that these causal variants result in low expression of CTLA4 (rho =-0.66, p = 1.28 x 10-38 and rho = -0.52, p = 7.01 x 10-22 , for rs231735 and rs231770 respectively).

INTERPRETATION: The disease relevant genes identified in this study are a unique resource for many disciplines including clinicians, scientists and the pharmaceutical industry. The distinct immunological pathways linked to early and late onset myasthenia gravis carry important implications for drug repurposing opportunities and for future studies of drug development. This article is protected by copyright. All rights reserved.

PMID:34279044 | DOI:10.1002/ana.26169

J Biomol Struct Dyn. 2021 Jul 19:1-10. doi: 10.1080/07391102.2021.1947895. Online ahead of print.

ABSTRACT

Since the first appearance of a novel coronavirus pneumonia (NCP) caused by a novel human coronavirus, and especially after the infection started its rapid spread over the world causing the COVID-19 (coronavirus disease 2019) pandemics, a very substantial part of the scientific community is engaged in the intensive research dedicated to finding of the potential therapeutics to cure this disease. As repurposing of existing drugs represents the only instant solution for those infected with the virus, we have been working on utilization of the structure-based virtual screening method to find some potential medications. In this study, we screened a library of 646 FDA approved drugs against the receptor-binding domain of the SARS-CoV-2 spike (S) protein and the main protease of this virus. Scoring functions revealed that some of the anticancer drugs (such as Pazopanib, Irinotecan, and Imatinib), antipsychotic drug (Risperidone), and antiviral drug (Raltegravir) have a potential to interact with both targets with high efficiency. Further we performed molecular dynamics simulations to understand the evolution in protein upon interaction with drug. Also, we have performed a phylogenetic analysis of 43 different coronavirus strains infecting 12 different mammalian species.Communicated by Ramaswamy H. Sarma.

PMID:34278954 | DOI:10.1080/07391102.2021.1947895

Patterns (N Y). 2021 Jul 14:100325. doi: 10.1016/j.patter.2021.100325. Online ahead of print.

ABSTRACT

An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (http://vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the drug primary targets and the SARS-CoV-2-host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with potential to act synergistically through different, yet potentially complementary pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on individual drugs or drug pairs. Overall, COVID-CDR is the first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.

PMID:34278363 | PMC:PMC8277549 | DOI:10.1016/j.patter.2021.100325

ACS Omega. 2021 Jun 25;6(27):17472-17482. doi: 10.1021/acsomega.1c01865. eCollection 2021 Jul 13.

ABSTRACT

The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx-efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC50) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and naïve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

PMID:34278133 | PMC:PMC8280707 | DOI:10.1021/acsomega.1c01865

Front Microbiol. 2021 Jun 30;12:653562. doi: 10.3389/fmicb.2021.653562. eCollection 2021.

ABSTRACT

With a continuous threat of antimicrobial resistance on human health worldwide, efforts for new alternatives are ongoing for the management of bacterial infectious diseases. Natural products of land and sea, being conceived to be having fewer side effects, pose themselves as a welcome relief. In this respect, we have taken a scaffolded approach to unearthing the almost unexplored chemical constituents of Malaysian red seaweed, Gracilaria edulis. Essentially, a preliminary evaluation of the ethyl acetate and acetone solvent extracts, among a series of six such, revealed potential antibacterial activity against six MDR species namely, Klebsiella pneumoniae, Pseudomonas aeruginosa, Salmonella enterica, methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, and Bacillus subtilis. Detailed analyses of the inlying chemical constituents, through LC-MS and GC-MS chromatographic separation, revealed a library of metabolic compounds. These were led for further virtual screening against selected key role playing proteins in the virulence of the aforesaid bacteria. To this end, detailed predictive pharmacological analyses added up to reinforce Eplerenone as a natural alternative from the plethora of plausible bioactives. Our work adds the ongoing effort to re-discover and repurpose biochemical compounds to combat the antimicrobial resistance offered by the Gram-positive and the -negative bacterial species.

PMID:34276590 | PMC:PMC8279767 | DOI:10.3389/fmicb.2021.653562

Front Pharmacol. 2021 Jun 30;12:617537. doi: 10.3389/fphar.2021.617537. eCollection 2021.

ABSTRACT

Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.

PMID:34276354 | PMC:PMC8277916 | DOI:10.3389/fphar.2021.617537