J Affect Disord. 2024 Nov 2:S0165-0327(24)01832-9. doi: 10.1016/j.jad.2024.11.002. Online ahead of print.

ABSTRACT

BACKGROUND: Neuroticism, known for its association with a greater risk of psychiatric conditions such as depression and anxiety, is a critical focus of research.

METHODS: Cis-expression quantitative trait loci (eQTLs) from 31,684 whole blood samples provided by the eQTLGen Consortium, alongside data from a large neuroticism cohort, were analyzed to identify genes causally linked to neuroticism. To further explore the influence of gene expression changes on neuroticism, colocalization analysis was conducted. Identified drug targets were assessed for potential side effects using a phenome-wide association study (PheWAS). Additionally, we utilized multiple databases to explore the interactions between drugs and genes for drug prediction and assess the current medications for drug repurposing.

RESULTS: The analysis involved a total of 4473 druggable genes, with two-sample Mendelian randomization (MR) identifying 186 genes that are causally linked to neuroticism. Colocalization analysis highlighted 11 genes (TLR4, MMRN1, EP300, BRAF, ORM1, ACVR1B, LRRC17, NOS2, ADAMTS6, GPX1, and VCL) with a posterior probability of colocalization (PPH4) >0.8. PheWAS revealed that drugs targeting BRAF, LRRC17, ADAMTS6, and GPX1 were also associated with other traits. Notably, six of these genes (TLR4, MMRN1, BRAF, ACVR1B, NOS2, and GPX1) are already being explored for drug development in psychiatric and other diseases.

CONCLUSION: This study pinpointed six genes as promising therapeutic targets for neuroticism. The repurposing and development of drugs targeting these genes hold potential for managing neuroticism and associated psychiatric disorders.

PMID:39491682 | DOI:10.1016/j.jad.2024.11.002

Lung Cancer. 2024 Oct 24;197:107996. doi: 10.1016/j.lungcan.2024.107996. Online ahead of print.

ABSTRACT

BACKGROUND: Metabolic re-wiring with preferential fatty acid oxidation has been observed in lung cancer cells. Whether the use of trimetazidine, an anti-anginal agent that inhibits fatty acid oxidation, alters clinical outcomes in ischemic heart disease (IHD) patients with lung cancers is unknown.

METHODS: We carried out this territory-wide, retrospective cohort study of 279,894 IHD patients prescribed with trimetazidine or long-acting oral nitrates in Hong Kong (population coverage of 7.5 millions, January 1999 - December 2020). A total of 6561 patients with pre-existing or de novo lung cancers were identified. Clinical outcomes of all-cause mortality were longitudinally compared between lung cancer patients who received trimetazidine (n = 547) versus non-users (control, n = 6014).

RESULTS: Over 902.9 ± 1410.6 days, lower incidence of deaths occurred in the trimetazidine group (79.0 %, n = 432/547) compared to controls (90.5 %, n = 5442/6014, P < 0.001). Kaplan-Meier analyses showed that trimetazidine use was associated with significantly higher survival from all-cause mortality in IHD patients (trimetazidine: mean survival = 1840.6 [95 %CI 1596.0-2085.3], versus control: 1056.7 [95 %CI 1011.3-1102.0] days, Log Rank = 69.4, P < 0.001). Cox regression showed that trimetazidine use was significantly associated with reduced risk of all-cause mortality in crude (HR = 0.60 [95 %CI: 0.53 to 0.68], P < 0.001) and multivariable models (HR = 0.65 [95 % CI: 0.57 to 0.74], P < 0.001). Pre-specified analyses amongst patients with pre-existing lung cancers yielded similar findings (HR = 0.49 [95 %CI: 0.35 to 0.67], P < 0.001). Survival benefits related to trimetazidine use was predominantly restricted to non-cardiovascular mortality (P < 0.001).

CONCLUSIONS: Trimetazidine use is associated with higher overall survival in IHD patients with lung cancers, particularly from non-cardiovascular death. These findings need to be confirmed by randomized controlled trials.

PMID:39490205 | DOI:10.1016/j.lungcan.2024.107996

Nat Commun. 2024 Nov 3;15(1):9496. doi: 10.1038/s41467-024-53903-0.

ABSTRACT

Classical Zn2+-dependent deac(et)ylases play fundamental regulatory roles in life and are well characterized in eukaryotes regarding their structures, substrates and physiological roles. In bacteria, however, classical deacylases are less well understood. We construct a Generalized Profile (GP) and identify thousands of uncharacterized classical deacylases in bacteria, which are grouped into five clusters. Systematic structural and functional characterization of representative enzymes from each cluster reveal high functional diversity, including polyamine deacylases and protein deacylases with various acyl-chain type preferences. These data are supported by multiple crystal structures of enzymes from different clusters. Through this extensive analysis, we define the structural requirements of substrate selectivity, and discovered bacterial de-D-/L-lactylases and long-chain deacylases. Importantly, bacterial deacylases are inhibited by archetypal HDAC inhibitors, as supported by co-crystal structures with the inhibitors SAHA and TSA, and setting the ground for drug repurposing strategies to fight bacterial infections. Thus, we provide a systematic structure-function analysis of classical deacylases in bacteria and reveal the basis of substrate specificity, acyl-chain preference and inhibition.

PMID:39489725 | DOI:10.1038/s41467-024-53903-0

Chem Phys Lipids. 2024 Nov 1:105449. doi: 10.1016/j.chemphyslip.2024.105449. Online ahead of print.

ABSTRACT

The triggering receptor expressed on myeloid cells 2 (TREM2) is an immunoreceptor that interacts with a wide range of non-protein ligands, and it has been implicated in infectious and non-infectious diseases. However, there is a limited understanding on how this receptor interacts with non-protein ligands and the potential of such information to develop new therapeutic drugs. Therefore, our study aimed to elucidate the interactions between TREM2 and its non-protein ligands. First, we searched PubChem and Protein Data Bank (PDB) for TREM2 structures and their corresponding non-protein ligands. Subsequently, these structures were employed in molecular docking and MM/GBSA simulations with the Maestro software and molecular dynamics in GROMACS software. TREM2 was subsequently subjected to druggable site prediction using CavityPlus and receptor-based drug repositioning via the DrugRep server. TREM2 interacts with high affinity with its 12 non-protein ligands, with affinity values ranging from -33.01kcal/mol for phosphatidylserine to -80.87kcal/mol for cardiolipin (CLP). In molecular dynamics simulations, homodimeric TREM2 bound more stably to its lipid ligands, such as CLP and PSF, whereas it was unstable when unbound. The interactions between the receptor and its non-protein ligands were driven by the complementarity determining regions (CDR) 1 and 2, that are present in the hydrophobic and positively charged regions, highlighting that the Y38-R98 region is fundamental for drugs targeting TREM2. Our data underscore the significance of TREM2's CDRs in recognizing its ligands, suggesting they as promising targets for prospective drug design studies.

PMID:39489390 | DOI:10.1016/j.chemphyslip.2024.105449

PLoS One. 2024 Nov 1;19(11):e0308517. doi: 10.1371/journal.pone.0308517. eCollection 2024.

ABSTRACT

Aberrant fucosylation is the hallmark of malignant cell transformation, leading to many cellular events, such as uncontrolled cell proliferation, angiogenesis, tumor cell invasion, and metastasis. This increased fucosylation is caused due to the over-expression of fucosyltransferases (FUTs) that catalyzes the transfer of the fucose (Fuc) residue from GDP-fucose (donor substrate) to various oligosaccharides, glycoproteins, and glycolipids (acceptor substrates). Hence, fucosyltransferases (FUTs) are considered as validated target for the drug discovery against on cancers. In the current study, a drug repurposing approach was deployed to identify new hits against fucosyltransferase 2 (FUT2), using computational and biophysical techniques. A library of 500 US-FDA approved drugs were screened in-silico against fucosyltransferase 2 (FUT2) donor and acceptor sites. Five drugs were predicted as hits, based on their significant docking scores (-5.8 to -8.2), and binding energies (-43 to -51.19 Kcal/mol). Furthermore, STD-NMR highlighted the epitope of these drugs in the binding site of fucosyltransferase 2 (FUT2). Simulation studies provided insights about the binding site of these drugs, and 4 of them, acarbose, ascorbic acid, ibuprofen, and enalaprilat dihydrate, were found as significant binders at the donor binding site of fucosyltransferase 2 (FUT2). Hence, the current study reports the repurposed drugs as potential hits against fucosyltransferase 2 (FUT2). These may be further studied through in-vitro and in-vivo inhibitory and mechanistic studies.

PMID:39485776 | DOI:10.1371/journal.pone.0308517

Mol Neurobiol. 2024 Nov 1. doi: 10.1007/s12035-024-04602-w. Online ahead of print.

ABSTRACT

Microglia/macrophages (MG/Mφ) play a central role in the pathogenesis of multiple sclerosis (MS). However, the intricacies of the immunomodulatory microenvironment in MS, particularly the heterogeneity and regulatory mechanisms of MG/Mφ subpopulations, remain elusive. The commonly used treatment options for MS have several drawbacks, such as significant side effects and uncertain efficacy. The exploration of developing new drugs targeting MG/Mφ for the treatment of MS remains to be investigated. We identified three distinct subpopulations of MG/Mφ, among which MG/Mφ_3 significantly increased as the experimental autoimmune encephalomyelitis (EAE) progressed. Ifenprodil and RO-25-6981 demonstrated notable inhibition of inflammatory factor expression, accompanied by reduced cytotoxicity. The interaction modes of these compounds with the common binding pocket in the GluN1b-GluN2B amino terminal domain heterodimer were elucidated. Virtual docking, based on the N-methyl-D-aspartate (NMDA) receptor, showed that homo-skeleton compounds of ifenprodil potentially exhibit low binding free energy with the receptor, including eliprodil and volinanserin. In vitro cell models corroborated the effective inhibition of inflammatory factor expression and minimal cytotoxicity of eliprodil and volinanserin. CoMFA (standard error of estimate = 0.378, R2 = 0.928, F values = 241.255, Prob. of R2 = 0) and topomer CoMFA (q2 = 0.553, q2 stderr = 0.77, intercept = - 1.48, r2 = 0.908, r2 stderr = 0.35) were established based on the inhibitors of NMDA receptor. The contour maps of CoMFA and topomer CoMFA models give structural information to improve the inhibitory function. This study underscores the involvement of MG/Mφ in inflammatory pathways during MS progression and offers promising compound candidates for MS therapy targeting MG/Mφ.

PMID:39485630 | DOI:10.1007/s12035-024-04602-w

J Drug Target. 2024 Nov 1:1-55. doi: 10.1080/1061186X.2024.2424895. Online ahead of print.

ABSTRACT

Since its emergence shortly after the discovery of penicillin, antibiotic resistance has escalated dramatically, posing a significant health threat and economic burden. Combating antibiotic resistance, especially in Gram-negative bacteria (GNB) and drug-resistant Mycobacterium tuberculosis, necessitates innovative research, substantial financial investment, and global cooperation to safeguard public health and develop sustainable solutions. Drug repositioning, or drug repurposing, involves identifying new therapeutic applications for existing drugs, utilizing their established safety profiles and pharmacological data to swiftly provide effective treatments against resistant pathogens. Several drugs, including otilonium bromide, penfluridol, eltrombopag, ibuprofen, and ceritinib, have demonstrated potent antibacterial activity against multidrug-resistant (MDR) bacteria. These drugs can disrupt biofilms, damage bacterial membranes, and inhibit bacterial growth. Furthermore, the combination of repurposed drugs with conventional antibiotics can reduce the required dosage of individual drugs, mitigate side effects, and delay the development of resistance, making it a promising strategy against MDR bacteria such as Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Escherichia coli. Despite its promise, drug repurposing faces challenges such as potential off-target effects, toxicity, and regulatory and intellectual property issues, necessitating rigorous evaluations and strategic solutions. This article aims to explore the potential of drug repurposing as a strategy to combat antibiotic resistance, examining its benefits, challenges, and future prospects. We address the legal, economic, and practical challenges associated with repurposing existing drugs, highlight successful examples, and propose solutions to enhance the efficacy and viability of this approach in combating MDR bacterial infections.

PMID:39485073 | DOI:10.1080/1061186X.2024.2424895

Res Sq [Preprint]. 2024 Oct 14:rs.3.rs-4869009. doi: 10.21203/rs.3.rs-4869009/v1.

ABSTRACT

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as SNCA , CTSB , LRRK2, DGKQ , CD38 and CD44 . Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87-0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.

PMID:39483867 | PMC:PMC11527220 | DOI:10.21203/rs.3.rs-4869009/v1

Int J Biol Macromol. 2024 Oct 29:137003. doi: 10.1016/j.ijbiomac.2024.137003. Online ahead of print.

ABSTRACT

The surge of drug-resistant Mycobacterium tuberculosis (DR-TB) impedes the World Health Organization's efforts in ending TB and calls for new therapeutic formulations. M. tuberculosis sirtuin-like protein Rv1151c is a bifunctional enzyme with both deacetylation and desuccinylation activities, which plays an important role in M. tuberculosis drug resistance and stress responses. Thus, it appears to be a promising target for the development of new TB therapeutics. In this study, we screened 31,057 ligand compounds from seven compound libraries in silico to identify inhibitors of Rv1151c. Ipragliflozin can bind to Rv1151c and interact stably. Ipragliflozin can change the acylation level of M. tuberculosis by inhibiting Rv1151c and effectively inhibit the growth of M. tuberculosis H37Rv and M. smegmatis. It can potentiate the first-front anti-TB drug isoniazid. As an antidiabetic drug, Ipragliflozin can be potentially included in the regimen to treat diabetes-tuberculosis comorbidity.

PMID:39481722 | DOI:10.1016/j.ijbiomac.2024.137003

Commun Biol. 2024 Oct 30;7(1):1413. doi: 10.1038/s42003-024-07107-3.

ABSTRACT

Biomedical network learning offers fresh prospects for expediting drug repositioning. However, traditional network architectures struggle to quantify the relationship between micro-scale drug spatial structures and corresponding macro-scale biomedical networks, limiting their ability to capture key pharmacological properties and complex biomedical information crucial for drug screening and therapeutic discovery. Moreover, challenges such as difficulty in capturing long-range dependencies hinder current network-based approaches. To address these limitations, we introduce the Spatial Hierarchical Network, modeling molecular 3D structures and biological associations into a unified network. We propose an end-to-end framework, SpHN-VDA, integrating spatial hierarchical information through triple attention mechanisms to enhance machine understanding of molecular functionality and improve the accuracy of virus-drug association identification. SpHN-VDA outperforms leading models across three datasets, particularly excelling in out-of-distribution and cold-start scenarios. It also exhibits enhanced robustness against data perturbation, ranging from 20% to 40%. It accurately identifies critical motifs for binding sites, even without protein residue annotations. Leveraging reliability of SpHN-VDA, we have identified 25 potential candidate drugs through gene expression analysis and CMap. Molecular docking experiments with the SARS-CoV-2 spike protein further corroborate the predictions. This research highlights the broad potential of SpHN-VDA to enhance drug repositioning and identify effective treatments for various diseases.

PMID:39478146 | DOI:10.1038/s42003-024-07107-3

Sci Rep. 2024 Oct 30;14(1):26119. doi: 10.1038/s41598-024-75742-1.

ABSTRACT

In the crucial process of drug discovery and repurposing, precise prediction of drug-target interactions (DTIs) is paramount. This study introduces a novel DTI prediction approach-Multi-Layer Graph Attention Neural Network (MLGANN), through a groundbreaking computational framework that effectively harnesses multi-source information to enhance prediction accuracy. MLGANN not only strides forward in constructing a multi-layer DTI network by capturing both direct interactions between drugs and targets as well as their multi-level information but also amalgamates Graph Convolutional Networks (GCN) with a self-attention mechanism to comprehensively integrate diverse data sources. This method exhibited significant performance surpassing existing approaches in comparative experiments, underscoring its immense potential in elevating the efficiency and accuracy of DTI predictions. More importantly, this study accentuates the significance of considering multi-source data information and network heterogeneity in the drug discovery process, offering new perspectives and tools for future pharmaceutical research.

PMID:39478027 | DOI:10.1038/s41598-024-75742-1

Sci Rep. 2024 Oct 30;14(1):26132. doi: 10.1038/s41598-024-74206-w.

ABSTRACT

Radiation-induced morphea (RIM) is a rare complication of radiotherapy presenting as inflammatory fibrosis, most commonly reported in breast cancer patients. As underlying disease mechanisms are not well understood, targeted therapies are lacking. Since fibroblasts are the key mediators of all fibroproliferative diseases, this study aimed to characterize patient-derived fibroblasts to identify therapeutic targets. We studied primary human control and RIM-fibroblasts on a functional and molecular basis, analyzed peripheral blood and tissue samples and conducted, based on our findings, a treatment attempt in one patient. In RIM, we identified a distinct myofibroblast phenotype reflected by increased alpha-smooth-muscle-actin (αSMA) expression, reduced proliferation and migration rates, and overexpression of osteopontin (OPN). Our RNA sequencing identified aberrant Myc activation as a potential disease driver in RIM fibroblasts, similar to previous findings in systemic sclerosis. Treatment with the anti-inflammatory drug mesalazine reversed the myofibroblast phenotype by targeting Myc. Based on these findings, a patient with RIM was successfully treated with mesalazine, resulting in reduced inflammation and pain and tissue softening, while serum OPN was halved. The present study provides a comprehensive characterization of RIM fibroblasts, suggests a disease-driving role for Myc, demonstrates promising antifibrotic effects of mesalazine and proposes OPN as a biomarker for RIM.

PMID:39477958 | DOI:10.1038/s41598-024-74206-w

In Vivo. 2024 Nov-Dec;38(6):2688-2695. doi: 10.21873/invivo.13746.

ABSTRACT

BACKGROUND/AIM: Non-small-cell lung cancer (NSCLC) is the most frequently diagnosed malignancy and the first cause of cancer-related death. Thus, finding alternative therapeutic options is crucial. Drug repurposing offers therapeutic options in a simplified and affordable manner, especially to cancer patients in developing countries. Several drugs including antihistamines and beta-adrenergic receptor blockers (beta-blockers) display antiproliferative properties on cancer cells. Interestingly, NSCLC patients who had used either antihistamines or beta-blockers showed improved response to chemotherapy or reduced mortality in comparison to non-users of any of these drugs. However, combination therapy is gaining substantial interest in many cancers including non-EGFR mutated NSCLC. Here, we investigated the antineoplastic effect of the combination of the antihistamine loratadine, the beta-blocker nebivolol, and the tyrosine-kinase inhibitor gefitinib on NSCLC cell lines.

MATERIALS AND METHODS: A-549 and NCI-H1975 cell lines were used. The effect of nebivolol, gefitinib, and loratadine on the metabolic activity was studied using the MTT assay. The inhibitory concentrations (IC20 and IC50) were calculated and used in the drug-combination experiments. Apoptosis was investigated using flow cytometry; and cell survival using the colony formation assay.

RESULTS: The combination nebivolol-loratadine-gefitinib produced a significant synergistic effect on inhibiting the metabolic activity and colony formation, as well as on promoting apoptosis in both cell lines. Noteworthy, the effect on the cell line carrying the EGFR mutation (NCI-H1975) was very similar to the cell line that does not exhibit such mutation (A-549 cells).

CONCLUSION: The nebivolol-gefitinib-loratadine combination may be a promising alternative for lung cancer treatment.

PMID:39477390 | DOI:10.21873/invivo.13746

Anticancer Res. 2024 Nov;44(11):4895-4903. doi: 10.21873/anticanres.17315.

ABSTRACT

BACKGROUND/AIM: Ovarian cancer remains a significant challenge due to its high mortality rate and poor prognosis, especially in advanced stages. Despite treatment advancements, issues with resistance and recurrence persist, highlighting the urgent need for new and effective therapies. This study aimed to evaluate fostamatinib, an oral spleen tyrosine kinase inhibitor initially developed for autoimmune diseases, as a potential treatment for ovarian cancer.

MATERIALS AND METHODS: The effects of fostamatinib on ovarian cancer cell lines were assessed using WST-1 assays for cell proliferation. Apoptosis was evaluated through TUNEL assays, DNA fragmentation analysis, and flow cytometry. Western blot analysis was used to detect cleavage of apoptotic proteins, including caspase-3 and PARP, and flow cytometry analyzed cell cycle changes.

RESULTS: Fostamatinib treatment resulted in a dose- and time-dependent reduction in ovarian cancer cell growth and induced apoptosis, as indicated by increased TUNEL-positive cells, DNA fragmentation, and rises in both early and late apoptosis. Western blot analysis showed increased cleavage of apoptotic proteins, including caspase-3 and PARP. Flow cytometry also demonstrated an increase in the sub-G1 phase of the cell cycle, further supporting apoptosis induction.

CONCLUSION: Fostamatinib, by inhibiting cell proliferation and inducing apoptosis, shows promise as a repurposed therapeutic agent for ovarian cancer, potentially offering a new approach to improve patient outcomes.

PMID:39477304 | DOI:10.21873/anticanres.17315

J Chem Inf Model. 2024 Oct 30. doi: 10.1021/acs.jcim.4c01445. Online ahead of print.

ABSTRACT

Target identification plays a critical role in preclinical drug development. The in silico approach has been developed and widely applied to assist medicinal chemists and pharmacologists in drug target identification. There are many target prediction web servers available today that have revealed both advantages and shortcomings in practical applications. Here, we present 3DSTarPred, a web server for three-dimensional (3D) shape similarity-based target prediction of small molecules. A benchmark study showed that 3DSTarPred achieved a target prediction success rate of 76.27%, which was higher than that of existing target prediction web servers. In addition, the performance of 3DSTarPred in the target prediction of diverse substructures/superstructures was also better than that of the existing target prediction web servers. In case studies, 3DSTarPred was used to identify the potential targets of two small molecules, one being kaempferol, a natural lead compound for the treatment of Alzheimer's disease (AD), and the other being sildenafil, a candidate for drug repurposing in AD. The case studies further demonstrated the reliability and success of 3DSTarPred in practice. The 3DSTarPred server is freely available at http://3dstarpred.pumc.wecomput.com.

PMID:39475556 | DOI:10.1021/acs.jcim.4c01445

F1000Res. 2024 Jul 26;12:1452. doi: 10.12688/f1000research.131508.2. eCollection 2023.

ABSTRACT

Background: The protein interaction between the viral surface S-glycoprotein and the host angiotensin converting enzyme-2 receptor (ACE2) is key to the virulent nature of SARS-CoV-2. The potential role that effective drug repurposing strategies may have to help stem the impact of future outbreaks has been brought to light in the recent COVID-19 pandemic. This study outlines a comprehensive approach towards in-silico drug discovery which aims to identify hit agents that can be suitably translated into a clinical setting. Methods: We use two different computational platforms to analyze the viral S-glycoprotein in its bound conformational state to the ACE2 receptor. We employed a comprehensive screening approach to shortlist compounds capable of binding to the viral target interface and corroborated these findings using both Schrödinger's Glide and AutoDock Vina. Molecular dynamic simulation studies further verified the stability of the interaction at the viral-host protein interface. Results: Lymecycline, pentagalloylglucose, polydatin, and hexoprenaline were identified as prime candidates for further studies given the robust and stable nature of their interaction at the viral-host interface and relevance for clinical testing. These agents were shown in a 100-nanosecond simulation trajectory to favorably disrupt key binding interactions at the viral-host interface and may potentially inhibit viral entry into host cells. In all hit molecules it was observed that inhibiting the interaction with the following key viral binding residues: Lys17, Gly496, Tyr 505, and key host residues: His34, Asp38, Lys353, played a critical role toward the inhibition of the viral-host protein interaction. Conclusions: Our study is unique in its comprehensive approach to identify agents that can bind to the S-glycoprotein-ACE2 interface using multiple computational platforms. Among the hit compounds shortlisted in this study, both lymecycline and hexoprenaline may be considered as candidates for preliminarily clinical studies to assess their therapeutic potential in the management of COVID-19 infections.

PMID:39474132 | PMC:PMC11519612 | DOI:10.12688/f1000research.131508.2

Epilepsy Behav. 2024 Oct 27;161:110109. doi: 10.1016/j.yebeh.2024.110109. Online ahead of print.

ABSTRACT

The treatment of status epilepticus (SE) has changed little in the last 20 years, largely because of the high risks and costs of new drug development for SE. Moreover, SE poses specific challenges to drug development, such as patient diversity, logistical hurdles, and the need for acute treatment strategies that differ from chronic seizure prevention. This has reduced the appetite of industry to develop new drugs in this area. Drug repurposing is an attractive approach to address this unmet need. It offers significant advantages, including reduced development time, lower costs, and higher success rates, compared to novel drug development. Here I demonstrate how novel methods integrating biological knowledge and computational methods can be applied to drug repurposing in status epilepticus. Biological approaches focus on addressing mechanisms underlying drug resistance in SE (using for example ketamine, tacrolimus and safinamide) and longer-term consequences (using for example omaveloxolone, celecoxib and losartan). Additionally, artificial intelligence platforms, such as ChatGPT, can rapidly generate promising drug lists, while in silico methods can analyze gene expression changes to predict molecular targets. Combining AI and in silico approaches has identified several candidate drugs, including metformin, sirolimus and riluzole, for SE treatment. Despite the promise of repurposing, challenges remain, such as intellectual property issues and regulatory barriers. Nonetheless, drug repurposing presents a viable solution to the high costs and slow progress of traditional drug development for SE. This paper is based on a presentation made at the 9th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures, in April 2024.

PMID:39467455 | DOI:10.1016/j.yebeh.2024.110109

Microbiol Immunol. 2024 Oct 28. doi: 10.1111/1348-0421.13178. Online ahead of print.

ABSTRACT

Bovine papillomavirus type 1 (BPV1) is an oncogenic virus that causes lesions and cancer in infected cattle. Despite being one of the most studied genotypes in the family and occurring in herds worldwide, there are currently no vaccines or drugs for its control. The viral E6 oncoprotein plays a crucial role in infection by this virus, making it a promising target for the development of new therapies. In this regard, we integrated structure-based virtual screening approaches, drug repositioning, and molecular dynamics to identify approved drugs with the potential to inhibit BPV1 E6. Our results reveal that Lumacaftor and MK-3207 are promising candidates for controlling BPV1 infection. The findings of this study may contribute to the development of E6 oncoprotein blockers in an accelerated and cost-effective manner.

PMID:39467039 | DOI:10.1111/1348-0421.13178

IEEE J Biomed Health Inform. 2024 Oct 28;PP. doi: 10.1109/JBHI.2024.3486730. Online ahead of print.

ABSTRACT

Computational drug repositioning which accelerates the process of drug development is able to reduce the cost in terms of time and money dramatically which brings promising and broad perspectives for the treatment of complex diseases. Heterogeneous networks fusion has been proposed to improve the performance of drug repositioning. Due to the difference and the specificity including the network structure and the biological function among different biological networks, it poses serious challenge on how to represent drug features and construct drug-disease associations in drug repositioning. Therefore, we proposed a novel drug repositioning method (ATDR) that employed attention transfer across different networks constructed by the deeply represented features integrated from biological networks to implement the disease-drug association prediction. Specifically, we first implemented the drug feature characterization with the graph representation of random surfing for different biological networks, respectively. Then, the drug network of deep feature representation was constructed with the aggregated drug informative features acquired by the multi-modal deep autoencoder on heterogeneous networks. Subsequently, we accomplished the drug-disease association prediction by transferring attention from the drug network to the drug-disease interaction network. We performed comprehensive experiments on different datasets and the results illustrated the outperformance of ATDR compared with other baseline methods and the predicted potential drug-disease interactions could aid in the drug development for disease treatments.

PMID:39466876 | DOI:10.1109/JBHI.2024.3486730

PLoS Genet. 2024 Oct 28;20(10):e1011458. doi: 10.1371/journal.pgen.1011458. Online ahead of print.

ABSTRACT

DPAGT1-CDG is a Congenital Disorder of Glycosylation (CDG) that lacks effective therapies. It is caused by mutations in the gene DPAGT1 which encodes the first enzyme in N-linked glycosylation. We used a Drosophila rough eye model of DPAGT1-CDG with an improperly developed, small eye phenotype. We performed a drug repurposing screen on this model using 1,520 small molecules that are 98% FDA/EMA-approved to find drugs that improved its eye. We identified 42 candidate drugs that improved the DPAGT1-CDG model. Notably from this screen, we found that pharmacological and genetic inhibition of the dopamine D2 receptor partially rescued the DPAGT1-CDG model. Loss of both dopamine synthesis and recycling partially rescued the model, suggesting that dopaminergic flux and subsequent binding to D2 receptors is detrimental under DPAGT1 deficiency. This links dopamine signaling to N-glycosylation and represents a new potential therapeutic target for treating DPAGT1-CDG. We also genetically validate other top drug categories including acetylcholine-related drugs, COX inhibitors, and an inhibitor of NKCC1. These drugs and subsequent analyses reveal novel biology in DPAGT1 mechanisms, and they may represent new therapeutic options for DPAGT1-CDG.

PMID:39466823 | DOI:10.1371/journal.pgen.1011458