Funct Integr Genomics. 2023 Jul 11;23(3):231. doi: 10.1007/s10142-023-01168-z.

ABSTRACT

Monkeypox is a viral zoonosis with symptoms that are reminiscent of those experienced in previous smallpox cases. The GSAID database (Global Initiative on Sharing Avian Influenza Data) was used to assess 630 genomes of MPXV. The phylogenetic study revealed six primary clades, as well as a smaller percentage in radiating clades. Individual clades that make up various nationalities may have formed as a result of a particular SNP hotspot type that mutated in a specific population. The most significant mutation based on a mutational hotspot analysis was found at G3729A and G5143A. The gene ORF138, which encodes the Ankyrin repeat (ANK) protein, was found to have the most mutations. This protein mediates molecular recognition via protein-protein interactions. It was shown that 243 host proteins interacted with 10 monkeypox proteins identified as the hub proteins E3, SPI2, C5, K7, E8, G6, N2, B14, CRMB, and A41 through 262 direct connections. The interaction with chemokine system-related proteins provides further evidence that the monkeypox virus suppresses human proteins to facilitate its survival against innate immunity. Several FDA-approved molecules were evaluated as possible inhibitors of F13, a significant envelope protein on the membrane of extracellular versions of the virus. A total of 2500 putative ligands were individually docked with the F13 protein. The interaction between the F13 protein and these molecules may help prevent the monkeypox virus from spreading. After being confirmed by experiments, these putative inhibitors could have an impact on the activity of these proteins and be used in monkeypox treatments.

PMID:37432480 | DOI:10.1007/s10142-023-01168-z

Bioimpacts. 2023;13(3):241-253. doi: 10.34172/bi.2023.24252. Epub 2023 Jan 1.

ABSTRACT

INTRODUCTION: Drug repurposing is an effective strategy for identifying the use of approved drugs for new therapeutic purposes. This strategy has received particular attention in the development of cancer chemotherapy. Considering that a growing body of evidence suggesting the cholesterol-lowering drug ezetimibe (EZ) may prevent the progression of prostate cancer, we investigated the effect of EZ alone and in combination with doxorubicin (DOX) on prostate cancer treatment.

METHODS: In this study, DOX and EZ were encapsulated within a PCL-based biodegradable nanoparticle. The physicochemical properties of drug containing nanoparticle based on PCL-PEG-PCL triblock copolymer (PCEC) have been exactly determined. The encapsulation efficiency and release behavior of DOX and EZ were also studied at two different pHs and temperatures.

RESULTS: The average size of nanoparticles (NPs) observed by field emission scanning electron microscopy (FE-SEM) was around 82±23.80 nm, 59.7±18.7 nm, and 67.6±23.8 nm for EZ@PCEC, DOX@PCEC, and DOX+EZ@PCEC NPs, respectively, which had a spherical morphology. In addition, DLS measurement showed a monomodal size distribution of around 319.9, 166.8, and 203 nm hydrodynamic diameters and negative zeta potential (-30.3, -6.14, and -43.8) mV for EZ@PCEC, DOX@PCEC, and DOX+EZ@PCEC NPs, respectively. The drugs were released from the NPs sustainably in a pH and temperature-dependent manner. Based on the MTT assay results, PCEC copolymer exhibited negligible cytotoxicity on the PC3 cell line. Therefore, PCEC was a biocompatible and suitable nano-vehicle for this study. The cytotoxicity of the DOX-EZ-loaded NPs on the PC3 cell line was higher than that of NPs loaded with single drugs. All the data confirmed the synergistic effect of EZ in combination with DOX as an anticancer drug. Furthermore, fluorescent microscopy and DAPI staining were performed to show the cellular uptake, and morphological changes-induced apoptosis of treated cells.

CONCLUSION: Overall, the data from the experiments represented the successful preparation of the nanocarriers with high encapsulation efficacy. The designed nanocarriers could serve as an ideal candidate for combination therapy of cancer. The results corroborated each other and presented successful EZ and DOX formulations containing PCEC NPs and their efficiency in treating prostate cancer.

PMID:37431480 | PMC:PMC10329752 | DOI:10.34172/bi.2023.24252

PLoS One. 2023 Jul 10;18(7):e0283425. doi: 10.1371/journal.pone.0283425. eCollection 2023.

ABSTRACT

Achondroplasia (ACH) is a common skeletal dysplasia characterized by a disproportionately short stature. We found that meclizine, which is an over-the-counter drug for motion sickness, inhibited the fibroblast growth factor receptor 3 (FGFR3) gene using a drug repositioning strategy, and meclizine 1 and 2 mg/kg/day promoted bone growth in a mouse model of ACH. A previous phase 1a clinical trial for children with ACH demonstrated that a single dose of meclizine 25 and 50 mg was safe and that the simulated plasma concentration achieved steady state approximately 10 days after the first dose. The current study aimed to evaluate the safety and pharmacokinetics (PK) of meclizine in children with ACH after a 14-day-repeated dose of meclizine. Twelve patients with ACH aged 5-10 years were enrolled. Meclizine 12.5 (cohort 1) and 25 mg/day (cohort 2) were administered after meals for 14 days, and adverse events (AEs) and PK were evaluated. No patient experienced serious AEs in either group. The average (95% confidential interval [CI]) maximum drug concentration (Cmax), peak drug concentration (Tmax), area under the curve (AUC) from 0 to 24 h, and terminal elimination half-life (t1/2) after a 14-day-repeated administration of meclizine (12.5 mg) were 167 (83-250) ng/mL, 3.7 (3.1-4.2) h, 1170 (765-1570) ng·h/mL, and 7.4 (6.7-8.0) h, respectively. The AUC0-6h after the final administration was 1.5 times that after the initial dose. Cmax and AUC were higher in cohort 2 than in cohort 1 in a dose-dependent manner. Regarding the regimen of meclizine 12.5 and 25 mg in patients < 20 kg and ≥ 20 kg, respectively, the average (95% CI) AUC0-24h was 1270 (1100-1440) ng·h/mL. Compartment models demonstrated that the plasma concentration of meclizine achieved at a steady state after the 14th administration. Long-term administration of meclizine 12.5 or 25 mg/day is recommended for phase 2 clinical trials in children with ACH.

PMID:37428729 | DOI:10.1371/journal.pone.0283425

Eur J Clin Microbiol Infect Dis. 2023 Jul 10. doi: 10.1007/s10096-023-04634-5. Online ahead of print.

ABSTRACT

We evaluated in vitro activity of 13 drugs used in the treatment of some non-communicable diseases via repurposing to determine their potential use in the treatment of Acinetobacter baumannii infections caused by susceptible and multidrug-resistant strains. A. baumannii is a multidrug-resistant Gram-negative bacteria causing nosocomial infections, especially in intensive care units. It has been identified in the WHO critical pathogen list and this emphasises urgent need for new treatment options. As the development of new therapeutics is expensive and time consuming, finding new uses of existing drugs via drug repositioning has been favoured. Antimicrobial susceptibility tests were conducted on all 13 drugs according to CLSI. Drugs with MIC values below 128 μg/mL and control antibiotics were further subjected to synergetic effect and bacterial time-kill analysis. Carvedilol-gentamicin (FICI 0.2813) and carvedilol-amlodipine (FICI 0.5625) were determined to have synergetic and additive effect, respectively, on the susceptible A. baumannii strain, and amlodipine-tetracycline (FICI 0.75) and amitriptyline-tetracycline (FICI 0.75) to have additive effect on the multidrug-resistant A. baumannii strain. Most remarkably, both amlodipine and amitriptyline reduced the MIC of multidrug-resistant, including some carbapenems, A. baumannii reference antibiotic tetracycline from 2 to 0.5 μg/mL, for 4-folds. All these results were further supported by bacterial time-kill assay and all combinations showed bactericidal activity, at certain hours, at 4XMIC. Combinations proposed in this study may provide treatment options for both susceptible and multidrug-resistant A. baumannii infections but requires further pharmacokinetics and pharmacodynamics analyses and in vivo re-evaluations using appropriate models.

PMID:37428238 | DOI:10.1007/s10096-023-04634-5

Microbiol Spectr. 2023 Jul 10:e0503122. doi: 10.1128/spectrum.05031-22. Online ahead of print.

ABSTRACT

Amid the mounting burden of multidrug-resistant (MDR) bacterial infections on health care worldwide, drug repurposing, a time and cost-effective strategy to identify new applications for drugs approved for other indications, can effectively fill the void in the current antibiotic pipeline. In this study, we have repurposed a topical antifungal agent, oxiconazole, in combination with gentamicin against skin infections caused by multidrug-resistant Staphylococcus aureus. Oxiconazole was identified as having antibacterial activity against S. aureus via whole-cell screening assays against clinically relevant bacterial pathogens. It exhibited a potent in vitro profile, including equipotent activity against clinical drug-susceptible and -resistant S. aureus and Enterococcus spp. Checkerboard assays and time-kill kinetics studies demonstrated its concentration-dependent killing and ability to synergize with the approved antibiotics daptomycin and gentamicin against susceptible and MDR S. aureus strains. Oxiconazole also significantly eradicated preformed S. aureus biofilms in vitro. Eventually, in an assessment of its ability to generate resistant S. aureus mutants via serial passaging, oxiconazole displayed an extremely low propensity for developing stable resistance in S. aureus. Its in vivo efficacy alone and in combination with synergistic antibiotics was assessed in a murine superficial skin infection model of S. aureus, where it strongly synergized with gentamicin, exhibiting superior activity to the untreated control and drug-alone treatment groups. Thus, oxiconazole can be repurposed as an antibacterial alone and in combination with gentamicin against susceptible and gentamicin-resistant S. aureus infections. IMPORTANCE Staphylococcus aureus, which causes the majority of nosocomial and community-acquired infections globally, is a WHO high-priority pathogen for antibiotic research and development. In addition to invasive infections, it is the causative agent of moderate to severe skin infections, with an increasing prevalence of infections caused by MDR strains such as methicillin-resistant S. aureus (MRSA). Our study highlights the repurposing of oxiconazole, a topical antifungal agent, as an ideal candidate for combination therapy with gentamicin against susceptible and drug-resistant S. aureus skin infections due to its extremely low propensity for resistance generation in S. aureus, activity against MDR strains, bactericidal killing kinetics alone and in combination, broad antifungal efficacy, and excellent safety and tolerability profile.

PMID:37428033 | DOI:10.1128/spectrum.05031-22

Brief Bioinform. 2023 Jul 10:bbad247. doi: 10.1093/bib/bbad247. Online ahead of print.

ABSTRACT

Studies have shown that the mechanism of action of many drugs is related to miRNA. In-depth research on the relationship between miRNA and drugs can provide theoretical foundations and practical approaches for various areas, such as drug target discovery, drug repositioning and biomarker research. Traditional biological experiments to test miRNA-drug susceptibility are costly and time-consuming. Thus, sequence- or topology-based deep learning methods are recognized in this field for their efficiency and accuracy. However, these methods have limitations in dealing with sparse topologies and higher-order information of miRNA (drug) feature. In this work, we propose GCFMCL, a model for multi-view contrastive learning based on graph collaborative filtering. To the best of our knowledge, this is the first attempt that incorporates contrastive learning strategy into the graph collaborative filtering framework to predict the sensitivity relationships between miRNA and drug. The proposed multi-view contrastive learning method is divided into topological contrastive objective and feature contrastive objective: (1) For the homogeneous neighbors of the topological graph, we propose a novel topological contrastive learning method via constructing the contrastive target through the topological neighborhood information of nodes. (2) The proposed model obtains feature contrastive targets from high-order feature information according to the correlation of node features, and mines potential neighborhood relationships in the feature space. The proposed multi-view comparative learning effectively alleviates the impact of heterogeneous node noise and graph data sparsity in graph collaborative filtering, and significantly enhances the performance of the model. Our study employs a dataset derived from the NoncoRNA and ncDR databases, encompassing 2049 experimentally validated miRNA-drug sensitivity associations. Five-fold cross-validation shows that the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPR) and F1-score (F1) of GCFMCL reach 95.28%, 95.66% and 89.77%, which outperforms the state-of-the-art (SOTA) method by the margin of 2.73%, 3.42% and 4.96%, respectively. Our code and data can be accessed at https://github.com/kkkayle/GCFMCL.

PMID:37427977 | DOI:10.1093/bib/bbad247

HCA Healthc J Med. 2020 Nov 28;1:385-389. doi: 10.36518/2689-0216.1164. eCollection 2020.

ABSTRACT

Description A greater than 65-year-old Caucasian woman receiving long-term anticoagulation with warfarin for atrial fibrillation experienced a sudden rise in an international normalized ratio (INR) after she was started on remdesivir for management of 2019 Novel Coronavirus (COVID-19). Patient INR was maintained within the target therapeutic range of 2-3 with a warfarin dose of 11 mg/week before starting remdesivir. After 2 days of remdesivir therapy, the patient's INR increased significantly and remained elevated during the 5 day course of remdesivir therapy. Patient required an interruption of her warfarin therapy for 7 days, and her INR did not return to the targeted therapeutic INR range of 2-3 until day 5 from the last dose of remdesivir, despite no warfarin administration. A comprehensive PubMed/MEDLINE search did not find published literature documenting interaction between warfarin and remdesivir. We describe the first case report, to our knowledge, documenting a potential drug interaction between warfarin and remdesivir. The authors found that there is a probable interaction between warfarin and remdesivir when applying the Adverse Drug Reaction Probability Scale, Naranjo Scale. To reduce the risk of bleeding associated with excessive anticoagulation, clinicians should closely monitor INR, and adjust the warfarin dose accordingly when patients are receiving remdesivir and warfarin concomitantly.

PMID:37426841 | PMC:PMC10327983 | DOI:10.36518/2689-0216.1164

Open Life Sci. 2023 Jul 7;18(1):20220637. doi: 10.1515/biol-2022-0637. eCollection 2023.

ABSTRACT

Since the outbreak of the novel coronavirus nearly 3 years ago, the world's public health has been under constant threat. At the same time, people's travel and social interaction have also been greatly affected. The study focused on the potential host targets of SARS-CoV-2, CD13, and PIKfyve, which may be involved in viral infection and the viral/cell membrane fusion stage of SARS-CoV-2 in humans. In this study, electronic virtual high-throughput screening for CD13 and PIKfyve was conducted using Food and Drug Administration-approved compounds in ZINC database. The results showed that dihydroergotamine, Saquinavir, Olysio, Raltegravir, and Ecteinascidin had inhibitory effects on CD13. Dihydroergotamine, Sitagliptin, Olysio, Grazoprevir, and Saquinavir could inhibit PIKfyve. After 50 ns of molecular dynamics simulation, seven compounds showed stability at the active site of the target protein. Hydrogen bonds and van der Waals forces were formed with target proteins. At the same time, the seven compounds showed good binding free energy after binding to the target proteins, providing potential drug candidates for the treatment and prevention of SARS-CoV-2 and SARS-CoV-2 variants.

PMID:37426619 | PMC:PMC10329278 | DOI:10.1515/biol-2022-0637

Acta Pharm Sin B. 2023 Jun;13(6):2559-2571. doi: 10.1016/j.apsb.2023.03.012. Epub 2023 Mar 22.

ABSTRACT

Existing traditional Chinese medicine (TCM)-related databases are still insufficient in data standardization, integrity and precision, and need to be updated urgently. Herein, an Encyclopedia of Traditional Chinese Medicine version 2.0 (ETCM v2.0, http://www.tcmip.cn/ETCM2/front/#/) was constructed as the latest curated database hosting 48,442 TCM formulas recorded by ancient Chinese medical books, 9872 Chinese patent drugs, 2079 Chinese medicinal materials and 38,298 ingredients. To facilitate the mechanistic research and new drug discovery, we improved the target identification method based on a two-dimensional ligand similarity search module, which provides the confirmed and/or potential targets of each ingredient, as well as their binding activities. Importantly, five TCM formulas/Chinese patent drugs/herbs/ingredients with the highest Jaccard similarity scores to the submitted drugs are offered in ETCM v2.0, which may be of significance to identify prescriptions/herbs/ingredients with similar clinical efficacy, to summarize the rules of prescription use, and to find alternative drugs for endangered Chinese medicinal materials. Moreover, ETCM v2.0 provides an enhanced JavaScript-based network visualization tool for creating, modifying and exploring multi-scale biological networks. ETCM v2.0 may be a major data warehouse for the quality marker identification of TCMs, the TCM-derived drug discovery and repurposing, and the pharmacological mechanism investigation of TCMs against various human diseases.

PMID:37425046 | PMC:PMC10326295 | DOI:10.1016/j.apsb.2023.03.012

Front Cell Infect Microbiol. 2023 Jun 23;13:1226054. doi: 10.3389/fcimb.2023.1226054. eCollection 2023.

ABSTRACT

[This corrects the article DOI: 10.3389/fcimb.2023.1132538.].

PMID:37424775 | PMC:PMC10329110 | DOI:10.3389/fcimb.2023.1226054

Expert Opin Drug Discov. 2023 Jul 10:1-13. doi: 10.1080/17460441.2023.2221025. Online ahead of print.

ABSTRACT

INTRODUCTION: Collaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community.

AREAS COVERED: In this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds.

EXPERT OPINION: Structure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files.

PMID:37424369 | DOI:10.1080/17460441.2023.2221025

Comput Biol Med. 2023 Jun 22;163:107199. doi: 10.1016/j.compbiomed.2023.107199. Online ahead of print.

ABSTRACT

Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.

PMID:37421738 | DOI:10.1016/j.compbiomed.2023.107199

Kidney Int. 2023 Jul 5:S0085-2538(23)00487-8. doi: 10.1016/j.kint.2023.06.029. Online ahead of print.

ABSTRACT

Fabry disease is a rare disorder caused by variations in the alpha-galactosidase gene. To a degree, Fabry disease is manageable via enzyme replacement therapy (ERT). By understanding the molecular basis of Fabry nephropathy (FN) and ERT's long-term impact, here we aimed to provide a framework for selection of potential disease biomarkers and drug targets. We obtained biopsies from eight control individuals and two independent FN cohorts comprising 16 individuals taken prior to and after up to ten years of ERT, and performed RNAseq analysis. Combining pathway-centered analyses with network-science allowed computation of transcriptional landscapes from four nephron compartments and their integration with existing proteome and drug-target interactome data. Comparing these transcriptional landscapes revealed high inter-cohort heterogeneity. Kidney compartment transcriptional landscapes comprehensively reflected differences in FN cohort characteristics. With exception of a few aspects, in particular arteries, early ERT in patients with classical Fabry could lastingly revert FN gene expression patterns to closely match that of control individuals. Pathways nonetheless consistently altered in both FN cohorts pre-ERT were mostly in glomeruli and arteries and related to the same biological themes. While keratinization-related processes in glomeruli were sensitive to ERT, a majority of alterations, such as transporter activity and responses to stimuli, remained dysregulated or reemerged despite ERT. Inferring an ERT-resistant genetic module of expressed genes identified 69 drugs for potential repurposing matching the proteins encoded by 12 genes. Thus, we identified and cross-validated ERT-resistant gene product modules that, when leveraged with external data, allowed estimating their suitability as biomarkers to potentially track disease course or treatment efficacy and potential targets for adjunct pharmaceutical treatment.

PMID:37419447 | DOI:10.1016/j.kint.2023.06.029

PLoS One. 2023 Jul 7;18(7):e0288335. doi: 10.1371/journal.pone.0288335. eCollection 2023.

ABSTRACT

Toxoplasmosis, caused by the obligate intracellular parasite Toxoplasma gondii, affects about one-third of the world's population and can cause severe congenital, neurological and ocular issues. Current treatment options are limited, and there are no human vaccines available to prevent transmission. Drug repurposing has been effective in identifying anti-T. gondii drugs. In this study, the screening of the COVID Box, a compilation of 160 compounds provided by the "Medicines for Malaria Venture" organization, was conducted to explore its potential for repurposing drugs to combat toxoplasmosis. The objective of the present work was to evaluate the compounds' ability to inhibit T. gondii tachyzoite growth, assess their cytotoxicity against human cells, examine their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, and investigate the potential of one candidate drug through an experimental chronic model of toxoplasmosis. Early screening identified 29 compounds that could inhibit T. gondii survival by over 80% while keeping human cell survival up to 50% at a concentration of 1 μM. The Half Effective Concentrations (EC50) of these compounds ranged from 0.04 to 0.92 μM, while the Half Cytotoxic Concentrations (CC50) ranged from 2.48 to over 50 μM. Almitrine was chosen for further evaluation due to its favorable characteristics, including anti-T. gondii activity at nanomolar concentrations, low cytotoxicity, and ADMET properties. Administering almitrine bismesylate (Vectarion®) orally at dose of 25 mg/kg/day for ten consecutive days resulted in a statistically significant (p < 0.001) reduction in parasite burden in the brains of mice chronically infected with T. gondii (ME49 strain). This was determined by quantifying the RNA of living parasites using real-time PCR. The presented results suggest that almitrine may be a promising drug candidate for additional experimental studies on toxoplasmosis and provide further evidence of the potential of the MMV collections as a valuable source of drugs to be repositioned for infectious diseases.

PMID:37418497 | DOI:10.1371/journal.pone.0288335

IEEE/ACM Trans Comput Biol Bioinform. 2023 Jul 7;PP. doi: 10.1109/TCBB.2023.3292883. Online ahead of print.

ABSTRACT

Due to the lengthy and costly process of new drug discovery, increasing attention has been paid to drug repositioning, i.e., identifying new drug-disease associations. Current machine learning methods for drug repositioning mainly leverage matrix factorization or graph neural networks, and have achieved impressive performance. However, they often suffer from insufficient training labels of inter-domain associations, while ignore the intra-domain associations. Moreover, they often neglect the importance of tail nodes that have few known associations, which limits their effectiveness in drug repositioning. In this paper, we propose a novel multi-label classification model with dual Tail-Node Augmentation for Drug Repositioning (TNA-DR). We incorporate disease-disease similarity and drug-drug similarity information into k-nearest neighbor ( kNN) augmentation module and contrastive augmentation module, respectively, which effectively complements the weak supervision of drug-disease associations. Furthermore, before employing the two augmentation modules, we filter the nodes by their degrees, so that the two modules are only applied to tail nodes. We conduct 10-fold cross validation experiments on four different real-world datasets, and our model achieves the state-of-the-art performance on all the four datasets. We also demonstrate our model's capability of identifying drug candidates for new diseases and discovering potential new links between existing drugs and diseases.

PMID:37418410 | DOI:10.1109/TCBB.2023.3292883

Mol Biol Rep. 2023 Jul 7. doi: 10.1007/s11033-023-08568-1. Online ahead of print.

ABSTRACT

Antiepileptic drugs are versatile drugs with the potential to be used in functional drug formulations with drug repurposing approaches. In the present review, we investigated the anticancer properties of antiepileptic drugs and interlinked cancer and epileptic pathways. Our focus was primarily on those drugs that have entered clinical trials with positive results and those that provided good results in preclinical studies. Many contributing factors make cancer therapy fail, like drug resistance, tumor heterogeneity, and cost; exploring all alternatives for efficient treatment is important. It is crucial to find new drug targets to find out new antitumor molecules from the already clinically validated and approved drugs utilizing drug repurposing methods. The advancements in genomics, proteomics, and other computational approaches speed up drug repurposing. This review summarizes the potential of antiepileptic drugs in different cancers and tumor progression in the brain. Valproic acid, oxcarbazepine, lacosamide, lamotrigine, and levetiracetam are the drugs that showed potential beneficial outcomes against different cancers. Antiepileptic drugs might be a good option for adjuvant cancer therapy, but there is a need to investigate further their efficacy in cancer therapy clinical trials.

PMID:37418080 | DOI:10.1007/s11033-023-08568-1

Clin Ther. 2023 Jun;45(6):527-534. doi: 10.1016/j.clinthera.2023.03.007.

ABSTRACT

PURPOSE: Interest in the use of cannabis as a medicine has markedly increased during the last decade, with an unprecedented number of patients now seeking advice or prescriptions for medicinal cannabis. Unlike other medicines prescribed by physicians, many medicinal cannabis products have not undergone standard clinical trial development required by regulatory authorities. Different formulations with varying strengths and ratios of tetrahydrocannabinol and cannabidiol are available, and this diversity of medicinal cannabis products available for a myriad of therapeutic indications adds to the complexity. Physicians face challenges and barriers in their clinical decision making with medicinal cannabis because of current evidence limitations. Research efforts to address evidence limitations are ongoing; in the interim, educational resources and clinical guidance are being developed to address the gap in clinical information and support the needs of health professionals.

METHODS: This article provides an overview of various resources that health professionals may use when seeking information about medicinal cannabis in the absence of high-quality evidence and clinical guidelines. It also identifies examples of international evidence-based resources that support clinical decision making with medicinal cannabis.

FINDINGS: Similarities and differences between international examples of guidance and guideline documents are identified and summarized.

IMPLICATIONS: Guidance can help guide physicians in the individualized choice and dose of medicinal cannabis. Before quality clinical trials and regulator-approved products with risk management programs, safety data require clinical and academic collaborative pharmacovigilance.

PMID:37414503 | DOI:10.1016/j.clinthera.2023.03.007

J Mycol Med. 2023 Jun 30;33(3):101411. doi: 10.1016/j.mycmed.2023.101411. Online ahead of print.

ABSTRACT

The increasing resistance to antifungal agents associated with toxicity and interactions turns therapeutic management of fungal infections difficult. This scenario emphasizes the importance of drug repositioning, such as nitroxoline - a urinary antibacterial agent that has shown potential antifungal activity. The aims of this study were to discover the possible therapeutic targets of nitroxoline using an in silico approach, and to determine the in vitro antifungal activity of the drug against the fungal cell wall and cytoplasmic membrane. We explored the biological activity of nitroxoline using PASS, SwissTargetPrediction and Cortellis Drug Discovery Intelligence web tools. After confirmation, the molecule was designed and optimized in HyperChem software. GOLD 2020.1 software was used to predict the interactions between the drug and the target proteins. In vitro investigation evaluated the effect of nitroxoline on the fungal cell wall through sorbitol protection assay. Ergosterol binding assay was carried out to assess the effect of the drug on the cytoplasmic membrane. In silico investigation revealed biological activity with alkane 1-monooxygenase and methionine aminopeptidase enzymes, showing nine and five interactions in the molecular docking, respectively. In vitro results exhibited no effect on the fungal cell wall or cytoplasmic membrane. Finally, nitroxoline has potential as an antifungal agent due to the interaction with alkane 1-monooxygenase and methionine aminopeptidase enzymes, which are not the main human therapeutic targets. These results have potentially revealed a new biological target for the treatment of fungal infections. We also consider that further studies are required to confirm the biological activity of nitroxoline on fungal cells, mainly the confirmation of the alkB gene.

PMID:37413753 | DOI:10.1016/j.mycmed.2023.101411

BMC Bioinformatics. 2023 Jul 6;24(1):278. doi: 10.1186/s12859-023-05387-w.

ABSTRACT

MOTIVATION: Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model.

RESULTS: To solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.

PMID:37415176 | DOI:10.1186/s12859-023-05387-w

OMICS. 2023 Jul 6. doi: 10.1089/omi.2023.0066. Online ahead of print.

ABSTRACT

Idiopathic pulmonary arterial hypertension (IPAH) is a progressive disease that affects the pulmonary arteries, resulting in increased pulmonary vascular resistance and right ventricular dysfunction, which can ultimately lead to heart failure and death. The molecular substrates of IPAH are poorly understood while diagnostics and therapeutics innovation remain as unmet needs for this debilitating disease. In this study, a network-based methodology was used to uncover the salient molecular mechanisms of IPAH to inform drug and diagnostic discovery, and personalized medicine. Expression profiling datasets associated with IPAH were obtained from the Gene Expression Omnibus database: GSE15197, GSE113439, GSE53408, and GSE67597. The comparative analysis of mRNA and miRNA expression data and the modular analysis of a transcriptome-based weighted gene coexpression network unraveled disease-specific gene and miRNA signatures. DEAD-box helicase 52 (DDx52), ESF1 nucleolar pre-RNA processing protein (ESF1), heterogeneous nuclear ribonuclearprotein A3 (MNRNPA3), Myosin VA (MYO5A), replication factor C subunit 1 (RFC1), and arginine and serine rich coiled coil 1 (RSRC1) were detected as the salient genes for IPAH. In addition, the salient gene-based drug repositioning analysis identified alvespimycin, tanespimycin, geldanamycin, LY294002, cephaeline, digoxigenin, lanatoside C, helveticoside, trichostatin A, phenoxybenzamine, genistein, pioglitazone, and rosiglitazone as potential drug candidates for IPAH. In conclusion, this study provides new molecular signatures in relation to IPAH and attendant potential drug candidates for further experimental and translational clinical research for patients with IPAH.

PMID:37410515 | DOI:10.1089/omi.2023.0066