Front Pharmacol. 2022 Nov 16;13:1030785. doi: 10.3389/fphar.2022.1030785. eCollection 2022.

NO ABSTRACT

PMID:36467064 | PMC:PMC9709317 | DOI:10.3389/fphar.2022.1030785

Front Pharmacol. 2022 Nov 18;13:1048691. doi: 10.3389/fphar.2022.1048691. eCollection 2022.

NO ABSTRACT

PMID:36467041 | PMC:PMC9715757 | DOI:10.3389/fphar.2022.1048691

Front Microbiol. 2022 Nov 17;13:1040252. doi: 10.3389/fmicb.2022.1040252. eCollection 2022.

NO ABSTRACT

PMID:36466666 | PMC:PMC9713947 | DOI:10.3389/fmicb.2022.1040252

Front Oncol. 2022 Nov 16;12:1029670. doi: 10.3389/fonc.2022.1029670. eCollection 2022.

NO ABSTRACT

PMID:36465398 | PMC:PMC9708744 | DOI:10.3389/fonc.2022.1029670

Signal Transduct Target Ther. 2022 Dec 5;7(1):387. doi: 10.1038/s41392-022-01249-8.

NO ABSTRACT

PMID:36464706 | DOI:10.1038/s41392-022-01249-8

Epilepsy Behav. 2022 Nov 29;138:108991. doi: 10.1016/j.yebeh.2022.108991. Online ahead of print.

NO ABSTRACT

PMID:36459813 | DOI:10.1016/j.yebeh.2022.108991

BMC Bioinformatics. 2022 Dec 1;23(1):516. doi: 10.1186/s12859-022-05069-z.

ABSTRACT

BACKGROUND: Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs.

METHODS: In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations.

RESULTS: To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.

PMID:36456957 | DOI:10.1186/s12859-022-05069-z

J Biomed Inform. 2022 Nov 28:104256. doi: 10.1016/j.jbi.2022.104256. Online ahead of print.

ABSTRACT

Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of causal effect, assuming that all populations have the same effect size. However, no "one-size-fits-all" treatment seems to work in some complex diseases. Treatment effects may vary by patient. Estimating heterogeneous treatment effects (HTE) may have a high impact on developing personalized treatment. Lots of advanced machine learning models for estimating HTE have emerged in recent years, but there has been limited translational research into the real-world healthcare domain. To fill the gap, we reviewed and compared eleven recent HTE estimation methodologies, including meta-learner, representation learning models, and tree-based models. We performed a comprehensive benchmark experiment based on nationwide healthcare claim data with application to Alzheimer's disease drug repurposing. We provided some challenges and opportunities in HTE estimation analysis in the healthcare domain to close the gap between innovative HTE models and deployment to real-world healthcare problems.

PMID:36455806 | DOI:10.1016/j.jbi.2022.104256

Toxicol Appl Pharmacol. 2022 Nov 28:116328. doi: 10.1016/j.taap.2022.116328. Online ahead of print.

ABSTRACT

Multiple sclerosis (MS) is an inflammatory and demyelinating disease of the central nervous system (CNS) that remains incurable. Herein, we demonstrated that ilepcimide (Antiepilepsirine), an antiepileptic drug used for decades, protects mice from experimental autoimmune encephalomyelitis (EAE), a mouse model of MS. Our studies found that ilepcimide treatment effectively ameliorates demyelination, blood-brain barrier leakage and infiltration of CD4+ and CD8+ T cells in EAE mice. On the one hand, ilepcimide can inhibit dihydroorotate dehydrogenase (DHODH), an important therapeutic target for MS. Computer molecular docking, thermal shift and fluorescence quenching assay demonstrated the directly interaction between ilepcimide and DHODH. Accordingly, ilepcimide observably repressed T cell proliferation in mixed lymphocyte reaction (MLR) assay and concanavalin A (Con-A) model in a DHODH-dependent manner. On the other hand, ilepcimide exhibited neuroprotective effect possibly through activating NRF2 antioxidant pathway in mouse neural crest-derived Neuro2a cells. Collectively, our findings have revealed the therapeutic potential of ilepcimide in EAE mouse model via restricting inflammatory response and oxidative stress, offering a potential opportunity for repurposing existing drug ilepcimide for MS therapy.

PMID:36455640 | DOI:10.1016/j.taap.2022.116328

AMB Express. 2022 Dec 1;12(1):151. doi: 10.1186/s13568-022-01488-6.

ABSTRACT

High incidences of urinary tract infection (UTI) of aminoglycosides-resistant E.coli causes a severe burden for public health. A new therapeutic strategy to ease this crisis is to repurpose non-antibacterial compounds to increase aminoglycosides sensibility against multidrug resistant E.coli pathogens. Based on high throughput screening technology, we profile the antimicrobial activity of tavaborole, a first antifungal benzoxaborole drug for onychomycosis treatment, and investigate the synergistic interaction between tavaborole and aminoglycosides, especially tobramycin and amikacin. Most importantly, by resistance accumulation assay, we found that, tavaborole not only slowed resistance occurrence of aminoglycosides, but also reduced invasiveness of E.coli in combination with tobramycin. Mechanistic studies preliminary explored that tavaborole and aminoglycosides lead to mistranslation, but would be still necessary to investigate more details for further research. In addition, tavaborole exhibited low systematic toxicity in vitro and in vivo, and enhanced aminoglycoside bactericidal activity in mice peritonitis model. Collectively, these results suggest the potential of tavaborole as a novel aminoglycosides adjuvant to tackle the clinically relevant drug resistant E. coli and encourages us to discover more benzoxaborole analogues for circumvention of recalcitrant infections.

PMID:36454354 | DOI:10.1186/s13568-022-01488-6

EMBO Mol Med. 2022 Dec 1:e17042. doi: 10.15252/emmm.202217042. Online ahead of print.

ABSTRACT

Drug repurposing, the strategy to identify new therapeutic use for clinically approved drugs has attracted much attention in recent years. This strategy offers various advantages over traditional approaches to develop new drugs, including shorter development timelines, low cost, and reduced risk of failure. In this issue of EMBO Molecular Medicine, Liu et al show that inosine monophosphate dehydrogenase (IMPDH) inhibitors, the well-known immunosuppressants have a potent therapeutic effect on the aggressive blood cancer, acute myeloid leukemia with MLL rearrangements. Intriguingly, the antileukemia effect of IMPDH inhibitors is mediated, at least in part through the overactivation of TLR signaling and Vcam1 upregulation. The robust antileukemia effect of IMPDH inhibitors, both in vitro and in vivo, together with their mechanistic findings provides a rational basis for repurposing IMPDH inhibitors for antileukemia therapy.

PMID:36453114 | DOI:10.15252/emmm.202217042

Chem Biol Drug Des. 2022 Nov 30. doi: 10.1111/cbdd.14187. Online ahead of print.

ABSTRACT

The development of inhibitors that target the papain-like protease (PLpro) has the potential to counteract the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent causing coronavirus disease 2019 (COVID-19). Based on a consideration of its several downstream effects, interfering with PLpro would both revert immune suppression exerted by the virus and inhibit viral replication. By following a repurposing strategy, the current study evaluates the potential of antimalarial drugs as PLpro inhibitors, and thereby the possibility of their use for treatment of SARS-CoV-2 infection. Computational tools were employed for structural analysis, molecular docking and molecular dynamics simulations to screen antimalarial drugs against PLpro, and in silico data were validated by in vitro experiments. Virtual screening highlighted amodiaquine and methylene blue as the best candidates, and these findings were complemented by the in vitro results that indicated amodiaquine as a μM PLpro deubiquitinase inhibitor. The results of this study demonstrate that the computational workflow adopted here can correctly identify active compounds. Thus, the highlighted antimalarial drugs represent a starting point for the development of new PLpro inhibitors through structural optimization.

PMID:36453012 | DOI:10.1111/cbdd.14187

Mol Brain. 2022 Nov 30;15(1):98. doi: 10.1186/s13041-022-00974-z.

ABSTRACT

The mechanism of assembly of RNA polymerase III (Pol III), the 17-subunit enzyme that synthesizes tRNAs, 5 S rRNA, and other small-nuclear (sn) RNAs in eukaryotes, is not clearly understood. The recent discovery of the HSP90 co-chaperone PAQosome (Particle for Arrangement of Quaternary structure) revealed a function for this machinery in the biogenesis of nuclear RNA polymerases. However, the connection between Pol III subunits and the PAQosome during the assembly process remains unexplored. Here, we report the development of a mass spectrometry-based assay that allows the characterization of Pol III assembly. This assay was used to dissect the stages of Pol III assembly, to start defining the function of the PAQosome in this process, to dissect the assembly defects driven by the leukodystrophy-causative R103H substitution in POLR3B, and to discover that riluzole, an FDA-approved drug for alleviation of ALS symptoms, partly corrects these assembly defects. Together, these results shed new light on the mechanism and regulation of human nuclear Pol III biogenesis.

PMID:36451185 | DOI:10.1186/s13041-022-00974-z

Sci Rep. 2022 Nov 30;12(1):20626. doi: 10.1038/s41598-022-23999-9.

ABSTRACT

Lippia sidoides Cham. (Verbenaceae) is a species often mentioned in traditional medicine due to the medicinal properties attributed to its leaves, which include antibacterial, antifungal, acaricidal and antioxidant. Several of these actions have been scientifically proven, according to reports in the literature; however, little is known about toxicological aspects of this plant. This work included studies to determine the chemical composition and toxicity tests, using several methods aiming to evaluate the safety for use of the aqueous extract of L. sidoides leaves, in addition, the anxiolytic effect on adult zebrafish was investigated, thus contributing to the pharmacological knowledge and traditional medicine concerning the specie under study. The chemical profile was determined by liquid chromatography coupled to mass spectrometry-HPLC/MS with electrospray ionization. Toxicity was evaluated by zebrafish, Drosophila melanogaster, blood cells, and Artemia salina models. 12 compounds belonging to the flavonoid class were identified. In the toxicity assays, the observed results showed low toxicity of the aqueous extract in all tests performed. In the analysis with zebrafish, the highest doses of the extract were anxiolytic, neuromodulating the GABAa receptor. The obtained results support the safe use of the aqueous extract of L. sidoides leaves for the development of new drugs and for the use by populations in traditional medicine.

PMID:36450779 | DOI:10.1038/s41598-022-23999-9

Cell Rep. 2022 Nov 29;41(9):111717. doi: 10.1016/j.celrep.2022.111717.

ABSTRACT

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.

PMID:36450252 | DOI:10.1016/j.celrep.2022.111717

Lung Cancer. 2022 Nov 23;175:36-46. doi: 10.1016/j.lungcan.2022.11.015. Online ahead of print.

ABSTRACT

OBJECTIVES: Lung cancer with distant metastases is associated with a very poor prognosis, and epithelial-mesenchymal transition (EMT) contributes to cancer metastasis. Therefore, elucidation and inhibition of EMT signaling in lung cancer may be a new therapeutic strategy for improving the prognosis of patients. We constructed a high-throughput screening system for EMT inhibitors. Using this system, we aimed to identify compounds that indeed inhibit EMT.

MATERIALS AND METHODS: We generated a luciferase reporter cell line using A549 human lung cancer cells and E-cadherin or vimentin as EMT markers. EMT was induced by transforming growth factor β1 (TGF-β1), and candidate EMT inhibitors were screened from a library of 2,350 compounds. The selected compounds were further tested using secondary assays to verify the inhibition of EMT and invasive capacity of cells.

RESULTS: Values obtained by the assay were adjusted for the number of viable cells and scored by determining the difference between mean values of the positive and negative control groups. Four compounds were identified as novel candidate drugs. Among those, one (avagacestat) and two compounds (GDC-0879 and levothyroxine) improved the expression of E-cadherin and vimentin, respectively, in epithelial cells. GDC-0879 and levothyroxine also significantly inhibited the invasive capacity of cells.

CONCLUSION: We systematically screened approved, investigational, and druggable compounds with inhibitory effects using a reporter assay, and identified candidate drugs for EMT inhibition.

PMID:36450215 | DOI:10.1016/j.lungcan.2022.11.015

J Biomol Struct Dyn. 2022 Nov 30:1-17. doi: 10.1080/07391102.2022.2151514. Online ahead of print.

ABSTRACT

Mycobacterium tuberculosis (Mtb) encodes a total of 67 PE_PGRS proteins and definite functions of many of them are still unknown. This study reports PE_PGRS45 (Rv2615c) protein from Mtb as NADPH dependent oxido-reductase having substrate specificity for fatty acyl Coenzyme A. Computational studies predicted PE_PGRS45 to be an integral membrane protein of Mtb. Expression of PE_PGRS45 in non-pathogenic Mycobacterium smegmatis, which does not possess PE_PGRS genes, confirmed its membrane localization. This protein was observed to have NADPH binding motif. Experimental validation confirmed its NADPH dependent oxido-reductase activity (Km value = 34.85 ± 9.478 μM, Vmax = 96.77 ± 7.184 nmol/min/mg of protein). Therefore, its potential to be targeted by first line anti-tubercular drug Isoniazid (INH) was investigated. INH was predicted to bind within the active site of PE_PGRS45 protein and experiments validated its inhibitory effect on the oxido-reductase activity of PE_PGRS45 with IC50/Ki values of 5.66 μM. Mtb is resistant to first line drugs including INH. Therefore, to address the problem of drug resistant TB, docking and Molecular Dynamics (MD) simulation studies between PE_PGRS45 and three drugs (Entacapone, Tolcapone and Verapamil) which are being used in Parkinson's and hypertension treatment were performed. PE_PGRS45 bound the three drugs with similar or better affinity in comparison to INH. Additionally, INH and these drugs bound within the same active site of PE_PGRS45. This study discovered Mtb's PE_PGRS45 protein to have an oxido-reductase activity and could be targeted by drugs that can be repurposed for TB treatment. Furthermore, in-vitro and in-vivo validation will aid in drug-resistant TB treatment. HIGHLIGHTSIn-silico and in-vitro studies of hypothetical protein PE_PGRS45 (Rv2615c) of Mycobacterium tuberculosis (Mtb) reveals it to be an integral membrane proteinPE_PGRS45 protein has substrate specificity for fatty acyl Coenzyme A (fatty acyl CoA) and possess NADPH dependent oxido-reductase activityDocking and simulation studies revealed that first line anti-tubercular drug Isoniazid (INH) and other drugs with anti-TB property have strong affinity for PE_PGRS45 proteinOxido-reductase activity of PE_PGRS45 protein is inhibited by INHPE_PGRS45 protein could be targeted by drugs that can be repurposed for TB treatmentCommunicated by Ramaswamy H. Sarma.

PMID:36448553 | DOI:10.1080/07391102.2022.2151514

IEEE/ACM Trans Comput Biol Bioinform. 2022 Nov 29;PP. doi: 10.1109/TCBB.2022.3225423. Online ahead of print.

ABSTRACT

Drug discovery and drug repurposing often rely on the successful prediction of drug-target interactions (DTIs). Recent advances have shown great promise in applying deep learning to drug-target interaction prediction. One challenge in building deep learning-based models is to adequately represent drugs and proteins that encompass the fundamental local chemical environments and long-distance information among amino acids of proteins (or atoms of drugs). Another challenge is to efficiently model the intermolecular interactions between drugs and proteins, which plays vital roles in the DTIs. To this end, we propose a novel model, GIFDTI, which consists of three key components: the sequence feature extractor (CNNFormer), the global molecular feature extractor (GF), and the intermolecular interaction modeling module (IIF). Specifically, CNNFormer incorporates CNN and Transformer to capture the local patterns and encode the long-distance relationship among tokens (atoms or amino acids) in a sequence. Then, GF and IIF extract the global molecular features and the intermolecular interaction features, respectively. We evaluate GIFDTI on six realistic evaluation strategies and the results show it improves DTI prediction performance compared to state-of-the-art methods. Moreover, case studies confirm that our model can be a useful tool to accurately yield low-cost DTIs. The codes of GIFDTI are available at https://github.com/zhaoqichang/GIFDTI.

PMID:36445997 | DOI:10.1109/TCBB.2022.3225423

IEEE/ACM Trans Comput Biol Bioinform. 2022 Nov 29;PP. doi: 10.1109/TCBB.2022.3225300. Online ahead of print.

ABSTRACT

Drug repositioning (DR) is a strategy to find new targets for existing drugs, which plays an important role in reducing the costs, time, and risk of traditional drug development. Recently, the matrix factorization approach has been widely used in the field of DR prediction. Nevertheless, there are still two challenges: 1) Learning ability deficiencies, the model cannot accurately predict more potential associations. 2) Easy to fall into a bad local optimal solution, the model tends to get a suboptimal result. In this study, we propose a self-paced non-negative matrix tri-factorization (SPLNMTF) model, which integrates three types of different biological data from patients, genes, and drugs into a heterogeneous network through non-negative matrix tri-factorization, thereby learning more information to improve the learning ability of the model. In the meantime, the SPLNMTF model sequentially includes samples into training from easy (high-quality) to complex (low-quality) in the soft weighting way, which effectively alleviates falling into a bad local optimal solution to improve the prediction performance of the model. The experimental results on two real datasets of ovarian cancer and acute myeloid leukemia (AML) show that SPLNMTF outperforms the other eight state-of-the-art models and gets better prediction performance in drug repositioning. The data and source code are available at: https://github.com/qi0906/SPLNMTF.

PMID:36445996 | DOI:10.1109/TCBB.2022.3225300

Brief Bioinform. 2022 Nov 29:bbac490. doi: 10.1093/bib/bbac490. Online ahead of print.

ABSTRACT

Transcriptome signature reversion (TSR) has been extensively proposed and used to discover new indications for existing drugs (i.e. drug repositioning, drug repurposing) for various cancer types. TSR relies on the assumption that a drug that can revert gene expression changes induced by a disease back to original, i.e. healthy, levels is likely to be therapeutically active in treating the disease. Here, we aimed to validate the concept of TSR using the PRISM repurposing data set, which is-as of writing-the largest pharmacogenomic data set. The predictive utility of the TSR approach as it has currently been used appears to be much lower than previously reported and is completely nullified after the drug gene expression signatures are adjusted for the general anti-proliferative downstream effects of drug-induced decreased cell viability. Therefore, TSR mainly relies on generic anti-proliferative drug effects rather than on targeting cancer pathways specifically upregulated in tumor types.

PMID:36445193 | DOI:10.1093/bib/bbac490