Front Immunol. 2024 Aug 9;15:1406041. doi: 10.3389/fimmu.2024.1406041. eCollection 2024.

ABSTRACT

BACKGROUND: Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the spine and sacroiliac joints. Recent genetic studies suggest certain plasma proteins may play a causal role in AS development. This study aims to identify and characterize these proteins using Mendelian randomization (MR) and colocalization analyses.

METHODS: Plasma protein data were obtained from recent publications in Nature Genetics, integrating data from five previous GWAS datasets, including 738 cis-pQTLs for 734 plasma proteins. GWAS summary data for AS were sourced from IGAS and other European cohorts. MR analyses were conducted using "TwoSampleMR" to assess causal links between plasma protein levels and AS. Colocalization analysis was performed with the coloc R package to identify shared genetic variants. Sensitivity analyses and protein-protein interaction (PPI) network analyses were conducted to validate findings and explore therapeutic targets. We performed Phenome-wide association study (PheWAS) to examine the potential side effects of drug protein on AS treatment.

RESULTS: After FDR correction, eight significant proteins were identified: IL7R, TYMP, IL12B, CCL8, TNFAIP6, IL18R1, IL23R, and ERAP1. Elevated levels of IL7R, IL12B, CCL8, IL18R1, IL23R, and ERAP1 increased AS risk, whereas elevated TYMP and TNFAIP6 levels decreased AS risk. Colocalization analysis indicated that IL23R, IL7R, and TYMP likely share causal variants with AS. PPI network analysis identified IL23R and IL7R as potential new therapeutic targets.

CONCLUSIONS: This study identified eight plasma proteins with significant associations with AS risk, suggesting IL23R, IL7R, and TYMP as promising therapeutic targets. Further research is needed to explore underlying mechanisms and potential for drug repurposing.

PMID:39185422 | PMC:PMC11341372 | DOI:10.3389/fimmu.2024.1406041

Res Sq [Preprint]. 2024 Aug 17:rs.3.rs-4750719. doi: 10.21203/rs.3.rs-4750719/v1.

ABSTRACT

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

PMID:39184100 | PMC:PMC11343300 | DOI:10.21203/rs.3.rs-4750719/v1

Microbiol Res. 2024 Aug 15;288:127879. doi: 10.1016/j.micres.2024.127879. Online ahead of print.

ABSTRACT

The emergence of plasmid-encoded colistin resistance mechanisms, MCR-1, a phosphoethanolamine transferase, rendered colistin ineffective as last resort antibiotic against severe infections caused by clinical Gram-negative bacterial pathogens. Through screening FDA-approved drug library, we identified two structurally similar compounds, namely cetylpyridinium chloride (CET) and domiphen bromide (DOM), which potentiated colistin activity in both colistin-resistant and susceptible Enterobacterales. These compounds were found to insert their long carbon chain to a hydrophobic pocket of bacterial phosphoethanolamine transferases including MCR-1, competitively blocking the binding of lipid A tail for substrate recognition and modification, resulting in the increase of bacterial sensitivity to colistin. In addition, these compounds were also found to dissipate bacterial membrane potential leading to the increase of bacterial sensitivity to colistin. Importantly, combinational use of DOM with colistin exhibited remarkable protection of test animals against infections by colistin-resistant bacteria in both mouse thigh infection and sepsis models. For mice infected by colistin-susceptible bacteria, the combinational use of DOM and colistin enable us to use lower dose of colistin to for efficient treatment. These properties render DOM excellent adjuvant candidates that help transform colistin into a highly potent antimicrobial agent for treatment of colistin-resistant Gram-negative bacterial infections and allowed us to use of a much lower dosage of colistin to reduce its toxicity against colistin-susceptible bacterial infection such as carbapenem-resistant Enterobacterales.

PMID:39182419 | DOI:10.1016/j.micres.2024.127879

Sci Rep. 2024 Aug 24;14(1):19670. doi: 10.1038/s41598-024-70758-z.

ABSTRACT

Observational studies have suggested the potential benefits of several medications for Parkinson's disease (PD) and their potential for repurposing. However, the conclusions drawn from these studies are not entirely consistent. To address this inconsistency, we used the two-sample Mendelian randomization (MR) method to explore the putative causal relationships between 23 medications and the risk and progression of PD. We applied inverse-variance weighted meta-analysis (IVW) to combine MR estimates. Additionally, sensitivity analyses were conducted to evaluate the robustness of the results. Our genetic evidence suggests that thyroid preparations and calcium channel blockers reduce the risk of PD, and salicylic acid and derivatives slow the progression of PD motor symptoms. Additionally, genetic evidence also suggests that four medications were associated with PD risk or progression, but the sensitivity analysis revealed that three of the medications may have interference caused by reverse causality. Our findings suggest that there are weak causal relationships between several medications and the risk or progression of PD. Though further replication studies are needed to verify these findings, these new insights may help in understanding the etiology of the disease, generate new clues related to drug discovery, and quantify the risk of future drug intake.

PMID:39181920 | DOI:10.1038/s41598-024-70758-z

J Cheminform. 2024 Aug 23;16(1):103. doi: 10.1186/s13321-024-00897-y.

ABSTRACT

MOTIVATION: Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies.

RESULTS: STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git .

PMID:39180095 | DOI:10.1186/s13321-024-00897-y

Nat Commun. 2024 Aug 24;15(1):7288. doi: 10.1038/s41467-024-51859-9.

ABSTRACT

Correctly identifying perturbed biological pathways is a critical step in uncovering basic disease mechanisms and developing much-needed therapeutic strategies. However, whether current tools are optimal for unbiased discovery of relevant pathways remains unclear. Here, we create "Benchmark" to critically evaluate existing tools and find that most function sub-optimally. We thus develop the "Pathway Ensemble Tool" (PET), which outperforms existing methods. Deploying PET, we identify prognostic pathways across 12 cancer types. PET-identified prognostic pathways offer additional insights, with genes within these pathways serving as reliable biomarkers for clinical outcomes. Additionally, normalizing these pathways using drug repurposing strategies represents therapeutic opportunities. For example, the top predicted repurposed drug for bladder cancer, a CDK2/9 inhibitor, represses cell growth in vitro and in vivo. We anticipate that using Benchmark and PET for unbiased pathway discovery will offer additional insights into disease mechanisms across a spectrum of diseases, enabling biomarker discovery and therapeutic strategies.

PMID:39179644 | DOI:10.1038/s41467-024-51859-9

Acta Crystallogr F Struct Biol Commun. 2024 Sep 1. doi: 10.1107/S2053230X24007039. Online ahead of print.

ABSTRACT

Crystallographic fragment screening has become a pivotal technique in structure-based drug design, particularly for bacterial targets with a crucial role in infectious disease mechanisms. The enzyme CdaA, which synthesizes an essential second messenger cyclic di-AMP (c-di-AMP) in many pathogenic bacteria, has emerged as a promising candidate for the development of novel antibiotics. To identify crystals suitable for fragment screening, CdaA enzymes from Streptococcus pneumoniae, Bacillus subtilis and Enterococcus faecium were purified and crystallized. Crystals of B. subtilis CdaA, which diffracted to the highest resolution of 1.1 Å, were used to perform the screening of 96 fragments, yielding data sets with resolutions spanning from 1.08 to 1.87 Å. A total of 24 structural hits across eight different sites were identified. Four fragments bind to regions that are highly conserved among pathogenic bacteria, specifically the active site (three fragments) and the dimerization interface (one fragment). The coordinates of the three active-site fragments were used to perform an in silico drug-repurposing screen using the OpenEye suite and the DrugBank database. This screen identified tenofovir, an approved drug, that is predicted to interact with the ATP-binding region of CdaA. Its inhibitory potential against pathogenic E. faecium CdaA has been confirmed by ITC measurements. These findings not only demonstrate the feasibility of this approach for identifying lead compounds for the design of novel antibacterial agents, but also pave the way for further fragment-based lead-optimization efforts targeting CdaA.

PMID:39177700 | DOI:10.1107/S2053230X24007039

Acta Crystallogr F Struct Biol Commun. 2024 Sep 1. doi: 10.1107/S2053230X24007027. Online ahead of print.

ABSTRACT

Brucella ovis is an etiologic agent of ovine epididymitis and brucellosis that causes global devastation in sheep, rams, goats, small ruminants and deer. There are no cost-effective methods for the worldwide eradication of ovine brucellosis. B. ovis and other protein targets from various Brucella species are currently in the pipeline for high-throughput structural analysis at the Seattle Structural Genomics Center for Infectious Disease (SSGCID), with the aim of identifying new therapeutic targets. Furthermore, the wealth of structures generated are effective tools for teaching scientific communication, structural science and biochemistry. One of these structures, B. ovis leucine-, isoleucine-, valine-, threonine- and alanine-binding protein (BoLBP), is a putative periplasmic amino acid-binding protein. BoLBP shares less than 29% sequence identity with any other structure in the Protein Data Bank. The production, crystallization and high-resolution structures of BoLBP are reported. BoLBP is a prototypical bacterial periplasmic amino acid-binding protein with the characteristic Venus flytrap topology of two globular domains encapsulating a large central cavity containing the peptide-binding region. The central cavity contains small molecules usurped from the crystallization milieu. The reported structures reveal the conformational flexibility of the central cavity in the absence of bound peptides. The structural similarity to other LBPs can be exploited to accelerate drug repurposing.

PMID:39177244 | DOI:10.1107/S2053230X24007027

Front Chem. 2024 Aug 8;12:1410882. doi: 10.3389/fchem.2024.1410882. eCollection 2024.

ABSTRACT

The exploration of non-cancer medications with potential anti-cancer activity offers a promising avenue for drug repurposing, accelerating the development of new oncological therapies. This study employs Quantitative Structure-Property Relationship (QSPR) modeling to identify and predict the anti-cancer efficacy of various non-cancer drugs, utilizing topological indices as key descriptors. Topological indices, which capture the molecular structure's geometric and topological characteristics, provide critical insights into the pharmacological interactions relevant to anti-cancer activity. By analyzing a comprehensive dataset of non-cancer medications, this research establishes robust QSPR models that correlate topological indices with anti-cancer activity. The models demonstrate significant predictive power, highlighting several non-cancer drugs with potential anti-cancer properties. Further, we will use linear, quadratic and logarithmic regression to understand the structures of anti-cancer drugs and strengthen our ability to manipulate the molecular structures. The findings underscore the utility of topological indices in drug repurposing strategies and pave the way for further experimental validation and clinical trials. This integrative approach enhances our understanding of drug action mechanisms and offers a cost-effective strategy for expanding the repertoire of anti-cancer agents.

PMID:39176073 | PMC:PMC11338857 | DOI:10.3389/fchem.2024.1410882

Life Sci. 2024 Aug 20:123001. doi: 10.1016/j.lfs.2024.123001. Online ahead of print.

ABSTRACT

Repurposing antidiabetic drugs for the treatment of Alzheimer's disease (AD) has emerged as a promising therapeutic strategy. This review examines the potential of repurposing antidiabetic drugs for AD treatment, focusing on preclinical evidence, clinical trials, and observational studies. In addition, the review aims to explore challenges and opportunities in repurposing antidiabetic drugs for AD, emphasizing the importance of well-designed clinical trials that consider patient selection criteria, refined outcome measures, adverse effects, and combination therapies to enhance therapeutic efficacy. Preclinical evidence suggests that glucagon-like peptide-1 (GLP-1) analogs, dipeptidyl peptidase-4 (DPP4) inhibitors, metformin, thiazolidinediones, and sodium-glucose co-transporter-2 (SGLT2) inhibitors exhibit neuroprotective effects in AD preclinical models. In preclinical studies, antidiabetic drugs have demonstrated neuroprotective effects by reducing amyloid beta (Aβ) plaques, tau hyperphosphorylation, neuroinflammation, and cognitive impairment. Antidiabetic drug classes, notably GLP-1 analogs and SGLT2 inhibitors, and a reduced risk of dementia in patients with diabetes mellitus. While the evidence for DPP4 inhibitors is mixed, some studies suggest a potential protective effect. On the other hand, alpha-glucosidase inhibitors (AGIs) and sulfonylureas may potentially increase the risk, especially in those experiencing recurrent hypoglycemic events. Repurposing antidiabetic drugs for AD is a promising therapeutic strategy, but challenges such as disease heterogeneity, limited biomarkers, and benefits versus risk evaluation need to be addressed. Ongoing clinical trials in mild cognitive impairment (MCI) and early AD patients without diabetes will be crucial in determining the clinical efficacy and safety of the antidiabetic drugs, paving the way for potential treatments for AD.

PMID:39173996 | DOI:10.1016/j.lfs.2024.123001

Int J Biol Macromol. 2024 Aug 20:134652. doi: 10.1016/j.ijbiomac.2024.134652. Online ahead of print.

ABSTRACT

Despite the WHO's recommended treatment regimen, challenges such as patient non-adherence and the emergence of drug-resistant strains persist with TB claiming 1.5 million lives annually. In this study, we propose a novel approach by targeting the DNA replication-machinery of M.tb through drug-repurposing. The β2-Sliding clamp (DnaN), a key component of this complex, emerges as a potentially vulnerable target due to its distinct structure and lack of human homology. Leveraging TBVS, we screened ~2600 FDA-approved drugs, identifying five potential DnaN inhibitors, by employing computational studies, including molecular-docking and molecular-dynamics simulations. The shortlisted compounds were subjected to in-vitro and ex-vivo studies, evaluating their anti-mycobacterial potential. Notably, Dicoumarol, Paromomycin, and Posaconazole exhibited anti-TB properties with a MIC value of 6.25, 3.12 and 50 μg/ml respectively, with Dicoumarol and Paromomycin, demonstrating efficacy in reducing live M.tb within macrophages. Biophysical analyses confirmed the strong binding-affinity of DnaNdrug complexes, validating our in-silico predictions. Moreover, RNA-Seq data revealed the upregulation of proteins associated with DNA repair and replication mechanisms upon Paromomycin treatment. This study explores repurposing FDA-approved drugs to target TB via the mycobacterial DNA replication-machinery, showing promising inhibitory effects. It sets the stage for further clinical research, demonstrating the potential of drug repurposing in TB treatment.

PMID:39173789 | DOI:10.1016/j.ijbiomac.2024.134652

Best Pract Res Clin Rheumatol. 2024 Aug 22:101988. doi: 10.1016/j.berh.2024.101988. Online ahead of print.

ABSTRACT

Rheumatic diseases, those that affect the musculoskeletal system, cause significant morbidity. Among risk factors of these diseases is a significant genetic component. Recent advances in high-throughput omics techniques now allow a comprehensive profiling of patients at a genetic level through genome-wide association studies. Without functional interpretation of variants identified through these studies, clinical insight remains limited. Strategies include statistical fine-mapping that refine the list of variants in loci associated with disease, whilst colocalization techniques attempt to attribute function to variants that overlap a genetically active chromatin annotation. Functional validation using genome editing techniques can be used to further refine genetic signals and identify key pathways in cell types relevant to rheumatic disease biology. Insight gained from the combination of genetic studies and functional validation can be used to improve precision medicine in rheumatic diseases by allowing risk prediction and drug repositioning.

PMID:39174375 | DOI:10.1016/j.berh.2024.101988

Med Res Rev. 2024 Aug 22. doi: 10.1002/med.22079. Online ahead of print.

ABSTRACT

Fenamates as classical nonsteroidal anti-inflammatory agents are widely used for relieving pain. Preclinical studies and epidemiological data highlight their chemo-preventive and chemotherapeutic potential for cancer. However, comprehensive reviews of fenamates in cancer are limited. To accelerate the repurposing of fenamates, this review summarizes the results of fenamates alone or in combination with existing chemotherapeutic agents. This paper also explores targets of fenamates in cancer therapy, including COX, AKR family, AR, gap junction, FTO, TEAD, DHODH, TAS2R14, ion channels, and DNA. Besides, this paper discusses other mechanisms, such as regulating Wnt/β-catenin, TGF-β, p38 MAPK, and NF-κB pathway, and the regulation of the expressions of Sp, EGR-1, NAG-1, ATF-3, ErbB2, AR, as well as the modulation of the tumor immune microenvironment. Furthermore, this paper outlined the structural modifications of fenamates, highlighting their potential as promising leads for anticancer drugs.

PMID:39171404 | DOI:10.1002/med.22079

Afr J Thorac Crit Care Med. 2024 Jul 4;30(2):e1352. doi: 10.7196/AJTCCM.2024.v30i2.1352. eCollection 2024.

ABSTRACT

ABSTRACT: Pulmonary hypertension (PH) is a haemodynamic disorder in which elevated blood pressure in the pulmonary circulation is caused by abnormal vascular tone. Despite advances in treatment, PH mortality remains high, and drug repurposing has been proposed as a mitigating approach. This article reviews the studies that have investigated drug repurposing as a viable option for PH. We provide an overview of PH and highlight pharmaceutical drugs with repurposing potential, based on limited evidence of their mechanisms of action. Moreover, studies have demonstrated the benefits of medicinal plants in PH, most of which are of Indian or Asian origin. Africa is a rich source of many medicinal plants that have been scientifically proven to counteract myriad pathologies. When perusing these studies, one will notice that some African medicinal plants can counteract the molecular pathways (e.g. proliferation, vasoconstriction, inflammation, oxidative stress and mitochondrial dysfunction) that are also involved in the pathogenesis of PH. We review the actions of these plants with actions applicable to PH and highlight that they could be repurposed as adjunct PH therapies. However, these plants have either never been tested in PH, or there is little evidence of their actions against PH. We therefore encourage caution, as more research is needed to study these plants further in experimental models of PH while acknowledging that the outcomes of such proof of-concept studies may not always yield promising findings. Regardless, this article aims to stimulate future research that could make timely contributions to the field.

STUDY SYNOPSIS: What the study adds. Pulmonary hypertension (PH) remains a fatal disease, and 80% of the patients live in developing countries where resources are scarce and specialised therapies are often unavailable. Drug repurposing is a viable option to try to improve treatment outcomes.Implications of the findings. We propose that another form of 'drug' repurposing is the use of medicinal plants, many of which have demonstrated benefits against pathological processes that are also key in PH, e.g. apoptosis, tumour-like growth of cells, proliferation, oxidative stress and mitochondrial dysfunction.

PMID:39171151 | PMC:PMC11334905 | DOI:10.7196/AJTCCM.2024.v30i2.1352

Chem Sci. 2024 Aug 19. doi: 10.1039/d4sc03580a. Online ahead of print.

ABSTRACT

The identification of targets for candidate molecules is a pivotal stride in the drug development journey, encompassing lead discovery, drug repurposing, and the scrutiny of potential off-target or side effects. Consequently, enhancing the precision of target prediction has significant implications. Moreover, current target prediction methods primarily rely on the principle of ligand-based chemical similarity, lacking the capture of novel compound-target relationships based on ligand high-level characterization similarity. Therefore, in this context, we introduce a pioneering algorithm known as the Fused Multiple Biological Signatures (FMBS) strategy. This approach leverages a Bayesian framework to amalgamate 25 predictable biological space characterizations of molecules to predict novel targets through scaffold hopping, thereby improving target prediction accuracy and providing a versatile tool for a wide range of small-molecule target prediction. When juxtaposed with alternative target prediction methods, FMBS showcases notable efficacy, outperforming traditional descriptors. Through an analysis of scaffold hopping cases, we elucidate how FMBS attains heightened accuracy by assimilating comprehensive and complementary high-dimensional signatures, thereby underscoring its potential in unearthing novel compound-target relationships. The findings underscore that our approach adeptly pinpoints promising candidate targets, thereby expediting drug mechanism exploration through the integration of multiple high-level characterizations.

PMID:39170720 | PMC:PMC11333953 | DOI:10.1039/d4sc03580a

Heliyon. 2024 Jul 29;10(15):e35342. doi: 10.1016/j.heliyon.2024.e35342. eCollection 2024 Aug 15.

ABSTRACT

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics.

METHODS: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival.

RESULTS: SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R 2 = 5.1 %; p-value = 3.2 × 10-27) and clinical characteristics.

CONCLUSIONS: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

PMID:39170265 | PMC:PMC11336650 | DOI:10.1016/j.heliyon.2024.e35342

Comput Biol Med. 2024 Aug 20;181:108988. doi: 10.1016/j.compbiomed.2024.108988. Online ahead of print.

ABSTRACT

Rosacea is a chronic dermatological condition that currently lacks a clear treatment approach due to an uncomprehensive knowledge of its pathogenesis. The main obstacle lies in understanding its etiology and the mode of action of the different drugs used. This study aims to clarify these aspects by employing drug repositioning. Using an in silico approach, we performed a transcriptomic analysis comparing samples from individuals with diverse types of rosacea to those from healthy controls to identify genes deregulated in this disease. Subsequently, we realized molecular docking and molecular dynamics studies to assess the binding affinity of drugs currently used to treat rosacea and drugs that target proteins interacting with, and thus affecting, proteins deregulated in rosacea. Our findings revealed that the downregulation of SKAP2 and upregulation of S100A7A in rosacea, could be involved in the pathogenesis of the disease. Furthermore, considering the drugs currently used for rosacea management, we demonstrated stable interactions between isotretinoin and BFH772 with SKAP2, and permethrin and PAC-14028 with S100A7A. Similarly, considering drugs targeting SKAP2 and S100A7A interactome proteins, we found that pitavastatin and dasatinib exert stable interactions with SKAP2, and lovastatin and tirbanibulin with S100A7A. In addition, we determine that the types of bonds involved in the interactions were different in SKAP2 from S100A7A. The drug-SKAP2 interactions are hydrogen bonds, whereas the drug-S100A7A interactions are of the hydrophobic type. In conclusion, our study provides evidence for the possible contribution of SKAP2 and S100A7A to rosacea pathology. Furthermore, it provides significant information on the molecular interactions between drugs and these proteins, highlighting the importance of considering structural features and binding interactions in the design of targeted therapies for skin disorders such as rosacea.

PMID:39168013 | DOI:10.1016/j.compbiomed.2024.108988

Sci Rep. 2024 Aug 21;14(1):19359. doi: 10.1038/s41598-024-68565-7.

ABSTRACT

The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins .

PMID:39169044 | DOI:10.1038/s41598-024-68565-7

J Autoimmun. 2024 Aug 19;148:103302. doi: 10.1016/j.jaut.2024.103302. Online ahead of print.

ABSTRACT

A balanced immune system is essential to maintain adequate host defense and effective self-tolerance. While an immune system that fails to generate appropriate response will permit infections to develop, uncontrolled activation may lead to autoinflammatory or autoimmune diseases. To identify drug candidates capable of modulating immune cell functions, we screened 1200 small molecules from the Prestwick Chemical Library for their property to inhibit innate or adaptive immune responses. Our studies focused specifically on drug interactions with T cells, B cells, and polymorphonuclear leukocytes (PMNs). Candidate drugs that were validated in vitro were examined in preclinical models to determine their immunomodulatory impact in chronic inflammatory diseases, here investigated in chronic inflammatory skin diseases. Using this approach, we identified several candidate drugs that were highly effective in preclinical models of chronic inflammatory disease. For example, we found that administration of pyrvinium pamoate, an FDA-approved over-the-counter anthelmintic drug, suppressed B cell activation in vitro and halted the progression of B cell-dependent experimental pemphigoid by reducing numbers of autoantigen-specific B cell responses. In addition, in studies performed in gene-deleted mouse strains provided additional insight into the mechanisms underlying these effects, for example, the receptor-dependent actions of tamoxifen that inhibit immune-complex-mediated activation of PMNs. Collectively, our methods and findings provide a vast resource that can be used to identify drugs that may be repurposed and used to promote or inhibit cellular immune responses.

PMID:39163739 | DOI:10.1016/j.jaut.2024.103302

J Exp Clin Cancer Res. 2024 Aug 20;43(1):234. doi: 10.1186/s13046-024-03150-4.

ABSTRACT

BACKGROUND: Colorectal cancer (CRC) is the third most common type of cancer and the second leading cause of cancer-related deaths worldwide, with a survival rate near to 10% when diagnosed at an advanced stage. Hence, the identification of new molecular targets to design more selective and efficient therapies is urgently required. The Mitogen activated protein kinase kinase 3 (MKK3) is a dual-specificity threonine/tyrosine protein kinase that, activated in response to cellular stress and inflammatory stimuli, regulates a plethora of biological processes. Previous studies revealed novel MKK3 roles in supporting tumor malignancy, as its depletion induces autophagy and cell death in cancer lines of different tumor types, including CRC. Therefore, MKK3 may represent an interesting new therapeutic target in advanced CRC, however selective MKK3 inhibitors are currently not available.

METHODS: The study involved transcriptomic based drug repurposing approach and confirmatory assays with CRC lines, primary colonocytes and a subset of CRC patient-derived organoids (PDO). Investigations in vitro and in vivo were addressed.

RESULTS: The repurposing approach identified the multitargeted kinase inhibitor AT9283 as a putative compound with MKK3 depletion-mimicking activities. Indeed, AT9283 drops phospho- and total-MKK3 protein levels in tested CRC models. Likely the MKK3 silencing, AT9283 treatment: i) inhibited cell proliferation promoting autophagy and cell death in tested CRC lines and PDOs; ii) resulted well-tolerated by CCD-18Co colonocytes; iii) reduced cancer cell motility inhibiting CRC cell migration and invasion; iv) inhibited COLO205 xenograft tumor growth. Mechanistically, AT9283 abrogated MKK3 protein levels mainly through the inhibition of aurora kinase A (AURKA), impacting on MKK3/AURKA protein-protein interaction and protein stability therefore uncovering the relevance of MKK3/AURKA crosstalk in sustaining CRC malignancy in vitro and in vivo.

CONCLUSION: Overall, we demonstrated that the anti-tumoral effects triggered by AT9283 treatment recapitulated the MKK3 depletion effects in all tested CRC models in vitro and in vivo, suggesting that AT9283 is a repurposed drug. According to its good tolerance when tested with primary colonocytes (CCD-18CO), AT9283 is a promising drug for the development of novel therapeutic strategies to target MKK3 oncogenic functions in late-stage and metastatic CRC patients.

PMID:39164711 | DOI:10.1186/s13046-024-03150-4