Sci Rep. 2024 Apr 24;14(1):9386. doi: 10.1038/s41598-024-59353-4.

ABSTRACT

Discovering effective anti-cancer agents poses a formidable challenge given the limited efficacy of current therapeutic modalities against various cancer types due to intrinsic resistance mechanisms. Cancer immunochemotherapy is an alternative strategy for breast cancer treatment and overcoming cancer resistance. Human Indoleamine 2,3-dioxygenase (hIDO1) and human Tryptophan 2,3-dioxygenase 2 (hTDO2) play pivotal roles in tryptophan metabolism, leading to the generation of kynurenine and other bioactive metabolites. This process facilitates the de novo synthesis of Nicotinamide Dinucleotide (NAD), promoting cancer resistance. This study identified a new dual hIDO1/hTDO2 inhibitor using a drug repurposing strategy of FDA-approved drugs. Herein, we delineate the development of a ligand-based pharmacophore model based on a training set of 12 compounds with reported hIDO1/hTDO2 inhibitory activity. We conducted a pharmacophore search followed by high-throughput virtual screening of 2568 FDA-approved drugs against both enzymes, resulting in ten hits, four of them with high potential of dual inhibitory activity. For further in silico and in vitro biological investigation, the anti-hypercholesterolemic drug Pitavastatin deemed the drug of choice in this study. Molecular dynamics (MD) simulations demonstrated that Pitavastatin forms stable complexes with both hIDO1 and hTDO2 receptors, providing a structural basis for its potential therapeutic efficacy. At nanomolar (nM) concentration, it exhibited remarkable in vitro enzyme inhibitory activity against both examined enzymes. Additionally, Pitavastatin demonstrated potent cytotoxic activity against BT-549, MCF-7, and HepG2 cell lines (IC50 = 16.82, 9.52, and 1.84 µM, respectively). Its anticancer activity was primarily due to the induction of G1/S phase arrest as discovered through cell cycle analysis of HepG2 cancer cells. Ultimately, treating HepG2 cancer cells with Pitavastatin affected significant activation of caspase-3 accompanied by down-regulation of cellular apoptotic biomarkers such as IDO, TDO, STAT3, P21, P27, IL-6, and AhR.

PMID:38653790 | DOI:10.1038/s41598-024-59353-4

Cell Rep Med. 2024 Apr 10:101521. doi: 10.1016/j.xcrm.2024.101521. Online ahead of print.

ABSTRACT

BCR::ABL1-independent pathways contribute to primary resistance to tyrosine kinase inhibitor (TKI) treatment in chronic myeloid leukemia (CML) and play a role in leukemic stem cell persistence. Here, we perform ex vivo drug screening of CML CD34+ leukemic stem/progenitor cells using 100 single drugs and TKI-drug combinations and identify sensitivities to Wee1, MDM2, and BCL2 inhibitors. These agents effectively inhibit primitive CD34+CD38- CML cells and demonstrate potent synergies when combined with TKIs. Flow-cytometry-based drug screening identifies mepacrine to induce differentiation of CD34+CD38- cells. We employ genome-wide CRISPR-Cas9 screening for six drugs, and mediator complex, apoptosis, and erythroid-lineage-related genes are identified as key resistance hits for TKIs, whereas the Wee1 inhibitor AZD1775 and mepacrine exhibit distinct resistance profiles. KCTD5, a consistent TKI-resistance-conferring gene, is found to mediate TKI-induced BCR::ABL1 ubiquitination. In summary, we delineate potential mechanisms for primary TKI resistance and non-BCR::ABL1-targeting drugs, offering insights for optimizing CML treatment.

PMID:38653245 | DOI:10.1016/j.xcrm.2024.101521

Comput Biol Med. 2024 Apr 17;175:108487. doi: 10.1016/j.compbiomed.2024.108487. Online ahead of print.

ABSTRACT

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.

PMID:38653064 | DOI:10.1016/j.compbiomed.2024.108487

Folia Microbiol (Praha). 2024 Apr 23. doi: 10.1007/s12223-024-01164-1. Online ahead of print.

ABSTRACT

Cryptococcosis is an invasive mycosis caused mainly by Cryptococcus gattii and C. neoformans and is treated with amphotericin B (AMB), fluconazole and 5-fluorocytosine. However, antifungal resistance, limited and toxic antifungal arsenal stimulate the search for therapeutic strategies such as drug repurposing. Among the repurposed drugs studied, the selective serotonin reuptake inhibitors (SSRIs) have shown activity against Cryptococcus spp. However, little is known about the antifungal effect of duloxetine hydrochloride (DH), a selective serotonin and norepinephrine reuptake inhibitor (SSNRI), against C. neoformans and C. gattii. In this study, DH inhibited the growth of several C. neoformans and C. gattii strains at concentrations ranging from 15.62 to 62.50 µg/mL. In addition, DH exhibited fungicidal activity ranging from 15.62 to 250 µg/mL. In biofilm, DH treatment reduced Cryptococcus spp. biomass at a level comparable to AMB, with a significant reduction (85%) for C. neoformans biofilms. The metabolic activity of C. neoformans and C. gattii biofilms decreased significantly (99%) after treatment with DH. Scanning electron micrographs confirmed the anti-biofilm activity of DH, as isolated cells could be observed after treatment. In conclusion, DH showed promising antifungal activity against planktonic cells and biofilms of C. neoformans and C. gattii, opening perspectives for further studies with DH in vivo.

PMID:38652436 | DOI:10.1007/s12223-024-01164-1

Daru. 2024 Apr 23. doi: 10.1007/s40199-024-00507-0. Online ahead of print.

ABSTRACT

PURPOSE: Identifying the molecular mechanisms behind SARS-CoV-2 disparities and similarities will help find new treatments. The present study determines networks' shared and non-shared (specific) crucial elements in response to HCoV-229E and SARS-CoV-2 viruses to recommend candidate medications.

METHODS: We retrieved the omics data on respiratory cells infected with HCoV-229E and SARS-CoV-2, constructed PPIN and GRN, and detected clusters and motifs. Using a drug-gene interaction network, we determined the similarities and disparities of mechanisms behind their host response and drug-repurposed.

RESULTS: CXCL1, KLHL21, SMAD3, HIF1A, and STAT1 were the shared DEGs between both viruses' protein-protein interaction network (PPIN) and gene regulatory network (GRN). The NPM1 was a specific critical node for HCoV-229E and was a Hub-Bottleneck shared between PPI and GRN in HCoV-229E. The HLA-F, ADCY5, TRIM14, RPF1, and FGA were the seed proteins in subnetworks of the SARS-CoV-2 PPI network, and HSPA1A and RPL26 proteins were the seed in subnetworks of the PPI network of HCOV-229E. TRIM14, STAT2, and HLA-F played the same role for SARS-CoV-2. Top enriched KEGG pathways included cell cycle and proteasome in HCoV-229E and RIG-I-like receptor, Chemokine, Cytokine-cytokine, NOD-like receptor, and TNF signaling pathways in SARS-CoV-2. We suggest some candidate medications for COVID-19 patient lungs, including Noscapine, Isoetharine mesylate, Cycloserine, Ethamsylate, Cetylpyridinium, Tretinoin, Ixazomib, Vorinostat, Venetoclax, Vorinostat, Ixazomib, Venetoclax, and epoetin alfa for further in-vitro and in-vivo investigations.

CONCLUSION: We suggested CXCL1, KLHL21, SMAD3, HIF1A, and STAT1, ADCY5, TRIM14, RPF1, and FGA, STAT2, and HLA-F as critical genes and Cetylpyridinium, Cycloserine, Noscapine, Ethamsylate, Epoetin alfa, Isoetharine mesylate, Ribavirin, and Tretinoin drugs to study further their importance in treating COVID-19 lung complications.

PMID:38652363 | DOI:10.1007/s40199-024-00507-0

Med Oncol. 2024 Apr 23;41(5):122. doi: 10.1007/s12032-024-02368-8.

ABSTRACT

Drug repositioning or repurposing has gained worldwide attention as a plausible way to search for novel molecules for the treatment of particular diseases or disorders. Drug repurposing essentially refers to uncovering approved or failed compounds for use in various diseases. Cancer is a deadly disease and leading cause of mortality. The search for approved non-oncologic drugs for cancer treatment involved in silico modeling, databases, and literature searches. In this review, we provide a concise account of the existing non-oncologic drug molecules and their therapeutic potential in chemotherapy. The mechanisms and modes of action of the repurposed drugs using computational techniques are also highlighted. Furthermore, we discuss potential targets, critical pathways, and highlight in detail the different challenges pertaining to drug repositioning for cancer immunotherapy.

PMID:38652344 | DOI:10.1007/s12032-024-02368-8

Epigenomes. 2024 Apr 1;8(2):14. doi: 10.3390/epigenomes8020014.

ABSTRACT

Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10-12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies.

PMID:38651367 | DOI:10.3390/epigenomes8020014

Front Oncol. 2024 Apr 8;14:1376515. doi: 10.3389/fonc.2024.1376515. eCollection 2024.

ABSTRACT

BACKGROUND: The role of drug repositioning in the treatment of ovarian cancer has received increasing attention. Although promising results have been achieved, there are also major controversies.

METHODS: In this study, we conducted a drug-target Mendelian randomisation (MR) analysis to systematically investigate the reported effects and relevance of traditional drugs in the treatment of ovarian cancer. The inverse-variance weighted (IVW) method was used in the main analysis to estimate the causal effect. Several MR methods were used simultaneously to test the robustness of the results.

RESULTS: By screening 31 drugs with 110 targets, FNTA, HSPA5, NEU1, CCND1, CASP1, CASP3 were negatively correlated with ovarian cancer, and HMGCR, PLA2G4A, ITGAL, PTGS1, FNTB were positively correlated with ovarian cancer.

CONCLUSION: Statins (HMGCR blockers), lonafarnib (farnesyltransferase inhibitors), the anti-inflammatory drug aspirin, and the anti-malarial drug adiponectin all have potential therapeutic roles in ovarian cancer treatment.

PMID:38651149 | PMC:PMC11033362 | DOI:10.3389/fonc.2024.1376515

Front Microbiol. 2024 Apr 8;15:1382953. doi: 10.3389/fmicb.2024.1382953. eCollection 2024.

ABSTRACT

Arenaviruses belonging to the Arenaviridae family, genus mammarenavirus, are enveloped, single-stranded RNA viruses primarily found in rodent species, that cause severe hemorrhagic fever in humans. With high mortality rates and limited treatment options, the search for effective antivirals is imperative. Current treatments, notably ribavirin and other nucleoside inhibitors, are only partially effective and have significant side effects. The high lethality and lack of treatment, coupled with the absence of vaccines for all but Junín virus, has led to the classification of these viruses as Category A pathogens by the Centers for Disease Control (CDC). This review focuses on entry inhibitors as potential therapeutics against mammarenaviruses, which include both New World and Old World arenaviruses. Various entry inhibition strategies, including small molecule inhibitors and neutralizing antibodies, have been explored through high throughput screening, genome-wide studies, and drug repurposing. Notable progress has been made in identifying molecules that target receptor binding, internalization, or fusion steps. Despite promising preclinical results, the translation of entry inhibitors to approved human therapeutics has faced challenges. Many have only been tested in in vitro or animal models, and a number of candidates showed efficacy only against specific arenaviruses, limiting their broader applicability. The widespread existence of arenaviruses in various rodent species and their potential for their zoonotic transmission also underscores the need for rapid development and deployment of successful pan-arenavirus therapeutics. The diverse pool of candidate molecules in the pipeline provides hope for the eventual discovery of a broadly effective arenavirus antiviral.

PMID:38650890 | PMC:PMC11033450 | DOI:10.3389/fmicb.2024.1382953

Bioinformatics. 2024 Apr 22:btae271. doi: 10.1093/bioinformatics/btae271. Online ahead of print.

ABSTRACT

MOTIVATION: Accurate inference of potential Drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance.

RESULTS: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multi-layer perceptrons (MLPs) to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach are expected to offer valuable insights for furthering drug repurposing and personalized medicine research.

AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:38648052 | DOI:10.1093/bioinformatics/btae271

J Cancer Res Clin Oncol. 2024 Apr 22;150(4):207. doi: 10.1007/s00432-024-05741-2.

ABSTRACT

PURPOSE: Colon cancer is a prevalent cancer globally, representing approximately 10% of all cancer cases and accounting for 10% of all cancer-related deaths. Therefore, finding new therapeutic methods with high efficiency will be very valuable. Cromolyn (C), a common anti-allergic and mast cell membrane stabilizing drug, has recently shown valuable anti-cancer effects in several studies. This study was designed to investigate the anti-cancer activity of cromolyn on colon cancer in vitro and in vivo and to determine values such as selectivity index and survival effect.

METHODS: HT-29 (colon cancer) and MCF-10 (normal epithelial) cell lines were treated with C and Doxorubicin (DOX; Positive control). IC50 values and the effects of C and DOX on apoptosis were explored using methyl thiazole diphenyl-tetrazolium bromide (MTT) assay and Annexin V/PI Apoptosis Assay Kit. To investigate in an animal study, colon cancer was subcutaneously induced by CT26 cells (mouse colon cancer) in bulb/c mice. Mice were treated with 0.05 LD50 intraperitoneal every other day for 35 days. After the death of mice, tumor volume, tumor weight, and survival rate were evaluated.

RESULTS: C selectively and significantly suppressed the proliferation of cancer cells in a dose-dependent manner. The IC50 values for the MCF-10 and HT29 cell lines were 7.33 ± 0.78 μM and 2.33 ± 0.6 μM, respectively. Notably, the selective index (SI) highlighted that C displayed greater selectivity in inhibiting cancer cell growth compared to DOX, with SI values of 3.15 and 2.60, respectively. C exhibited higher effectiveness and selectivity in inducing apoptosis in cancer cells compared to DOX, with a significant p-value (61% vs. 52%, P-value ≤ 0.0001). Also, in mice bearing colon cancer, C reduced the tumor volume (6317 ± 1685mm3) and tumor weight (9.8 ± 1.6 g) compared to the negative control group (weight 12.45 ± 0.9 g; volume 7346 ± 1077) but these values were not statistically significant (P ≤ 0.05).

CONCLUSION: Our study showed that cromolyn is a selective and strong drug in inhibiting the proliferation of colon cancer cells. Based on our results, the efficacy of C in vitro analysis (MTT assays and apoptosis), as well as animal studies is competitive with the FDA-approved drug doxorubicin. C is very promising as a low-complication and good-efficacy drug for cancer drug repositioning. This requires clinical research study designs to comprehensively evaluate its anti-cancer effects.

PMID:38647571 | DOI:10.1007/s00432-024-05741-2

Brief Bioinform. 2024 Mar 27;25(3):bbae172. doi: 10.1093/bib/bbae172.

ABSTRACT

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

PMID:38647153 | PMC:PMC11033846 | DOI:10.1093/bib/bbae172

Brief Bioinform. 2024 Mar 27;25(3):bbae108. doi: 10.1093/bib/bbae108.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leading causes of cancer-related deaths worldwide. The lack of clear treatment directions underscores the urgent need for innovative approaches to address and manage this deadly condition. In this research, we repurpose drugs with potential anti-cancer activity using machine learning (ML).

METHODS: We tackle the problem by using a neural network trained on drug-target interaction information enriched with drug-drug interaction information, which has not been used for anti-cancer drug repurposing before. We focus on eravacycline, an antibacterial drug, which was selected and evaluated to assess its anti-cancer effects.

RESULTS: Eravacycline significantly inhibited the proliferation and migration of BxPC-3 cells and induced apoptosis.

CONCLUSION: Our study highlights the potential of drug repurposing for cancer treatment using ML. Eravacycline showed promising results in inhibiting cancer cell proliferation, migration and inducing apoptosis in PDAC. These findings demonstrate that our developed ML drug repurposing models can be applied to a wide range of new oncology therapeutics, to identify potential anti-cancer agents. This highlights the potential and presents a promising approach for identifying new therapeutic options.

PMID:38647152 | PMC:PMC11033730 | DOI:10.1093/bib/bbae108

Patterns (N Y). 2024 Mar 5;5(4):100949. doi: 10.1016/j.patter.2024.100949. eCollection 2024 Apr 12.

ABSTRACT

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing.

PMID:38645769 | PMC:PMC11026976 | DOI:10.1016/j.patter.2024.100949

medRxiv [Preprint]. 2024 Apr 9:2024.04.06.24305166. doi: 10.1101/2024.04.06.24305166.

ABSTRACT

There is considerable comorbidity across externalizing and internalizing behavior dimensions of psychopathology. We applied genomic structural equation modeling (gSEM) to genome-wide association study (GWAS) summary statistics to evaluate the factor structure of externalizing and internalizing psychopathology across 16 traits and disorders among European-ancestry individuals (n's = 16,400 to 1,074,629). We conducted GWAS on factors derived from well-fitting models. Downstream analyses served to identify biological mechanisms, explore drug repurposing targets, estimate genetic overlap between the externalizing and internalizing spectra, and evaluate causal effects of psychopathology liability on physical health. Both a correlated factors model, comprising two factors of externalizing and internalizing risk, and a higher-order single-factor model of genetic effects contributing to both spectra demonstrated acceptable fit. GWAS identified 409 lead single nucleotide polymorphisms (SNPs) associated with externalizing and 85 lead SNPs associated with internalizing, while the second-order GWAS identified 256 lead SNPs contributing to broad psychopathology risk. In bivariate causal mixture models, nearly all externalizing and internalizing causal variants overlapped, despite a genetic correlation of only 0.37 (SE = 0.02) between them. Externalizing genes showed cell-type specific expression in GABAergic, cortical, and hippocampal neurons, and internalizing genes were associated with reduced subcallosal cortical volume, providing insight into the neurobiological underpinnings of psychopathology. Genetic liability for externalizing, internalizing, and broad psychopathology exerted causal effects on pain, general health, cardiovascular diseases, and chronic illnesses. These findings underscore the complex genetic architecture of psychopathology, identify potential biological pathways for the externalizing and internalizing spectra, and highlight the physical health burden of psychiatric comorbidity.

PMID:38645045 | PMC:PMC11030494 | DOI:10.1101/2024.04.06.24305166

Front Cell Neurosci. 2024 Apr 4;18:1367838. doi: 10.3389/fncel.2024.1367838. eCollection 2024.

ABSTRACT

Variants in KCNT1 are associated with a wide spectrum of epileptic phenotypes, including epilepsy of infancy with migrating focal seizures (EIMFS), non-EIMFS developmental and epileptic encephalopathies, autosomal dominant or sporadic sleep-related hypermotor epilepsy, and focal epilepsy. Here, we describe a girl affected by drug-resistant focal seizures, developmental delay and behavior disorders, caused by a novel, de novo heterozygous missense KCNT1 variant (c.2809A > G, p.S937G). Functional characterization in transiently transfected Chinese Hamster Ovary (CHO) cells revealed a strong gain-of-function effect determined by the KCNT1 p.S937G variant compared to wild-type, consisting in an increased maximal current density and a hyperpolarizing shift in current activation threshold. Exposure to the antidepressant drug fluoxetine inhibited currents expressed by both wild-type and mutant KCNT1 channels. Treatment of the proband with fluoxetine led to a prolonged electroclinical amelioration, with disappearance of seizures and better EEG background organization, together with an improvement in behavior and mood. Altogether, these results suggest that, based on the proband's genetic and functional characteristics, the antidepressant drug fluoxetine may be repurposed for the treatment of focal epilepsy caused by gain-of-function variants in KCNT1. Further studies are needed to verify whether this approach could be also applied to other phenotypes of the KCNT1-related epilepsies spectrum.

PMID:38644974 | PMC:PMC11027738 | DOI:10.3389/fncel.2024.1367838

Front Oncol. 2024 Apr 4;14:1403778. doi: 10.3389/fonc.2024.1403778. eCollection 2024.

NO ABSTRACT

PMID:38638856 | PMC:PMC11024629 | DOI:10.3389/fonc.2024.1403778

Signal Transduct Target Ther. 2024 Apr 19;9(1):92. doi: 10.1038/s41392-024-01808-1.

ABSTRACT

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

PMID:38637540 | DOI:10.1038/s41392-024-01808-1

Comput Biol Med. 2024 Apr 15;174:108444. doi: 10.1016/j.compbiomed.2024.108444. Online ahead of print.

ABSTRACT

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

PMID:38636325 | DOI:10.1016/j.compbiomed.2024.108444

J Med Internet Res. 2024 Apr 18;26:e46777. doi: 10.2196/46777.

ABSTRACT

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs.

OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics.

METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base.

RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones.

CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

PMID:38635981 | DOI:10.2196/46777