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

Arch Microbiol. 2024 Aug 20;206(9):383. doi: 10.1007/s00203-024-04103-3.

ABSTRACT

Candida albicans has been listed in the critical priority group by the WHO in 2022 depending upon its contribution in invasive candidiasis and increased resistance to conventional drugs. Drug repurposing offers an efficient, rapid, and cost-effective solution to develop alternative therapeutics against pathogenic microbes. Alexidine dihydrochloride (AXD) and hexachlorophene (HCP) are FDA approved anti-cancer and anti-septic drugs, respectively. In this study, we have shown antifungal properties of AXD and HCP against the wild type (reference strain) and clinical isolates of C. albicans. The minimum inhibitory concentrations (MIC50) of AXD and HCP against C. albicans ranged between 0.34 and 0.69 µM and 19.66-24.58 µM, respectively. The biofilm inhibitory and eradication concentration of AXD was reported comparatively lower than that of HCP for the strains used in the study. Further investigations were performed to understand the antifungal mode of action of AXD and HCP by studying virulence features like cell surface hydrophobicity, adhesion, and yeast to hyphae transition, were also reduced upon exposure to both the drugs. Ergosterol content in cell membrane of the wild type strain was upregulated on exposure to AXD and HCP both. Biochemical analyses of the exposed biofilm indicated reduced contents of carbohydrate, protein, and e-DNA in the extracellular matrix of the biofilm when compared to the untreated control biofilm. AXD exposure downregulated activity of tissue invading enzyme, phospholipase in the reference strain. In wild type strain, ROS level, and activities of antioxidant enzymes were found elevated upon exposure to both drugs. FESEM analysis of the drug treated biofilms revealed degraded biofilm. This study has indicated mode of action of antifungal potential of alexidine dihydrochloride and hexachlorophene in C. albicans.

PMID:39162873 | DOI:10.1007/s00203-024-04103-3

Microbiol Spectr. 2024 Aug 20:e0124924. doi: 10.1128/spectrum.01249-24. Online ahead of print.

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to cause severe disease and deaths in many parts of the world, despite massive vaccination efforts. Antiviral drugs to curb an ongoing infection remain a priority. The virus-encoded 3C-like main protease (MPro; nsp5) is seen as a promising target. Here, with a positive selection genetic system engineered in Saccharomyces cerevisiae using cleavage and release of MazF toxin as an indicator, we screened in a robotized setup small molecule libraries comprising ~2,500 compounds for MPro inhibitors. We detected eight compounds as effective against MPro expressed in yeast, five of which are characterized proteasome inhibitors. Molecular docking indicates that most of these bind covalently to the MPro catalytically active cysteine. Compounds were confirmed as MPro inhibitors in an in vitro enzymatic assay. Among those were three previously only predicted in silico; the boron-containing proteasome inhibitors bortezomib, delanzomib, and ixazomib. Importantly, we establish reaction conditions in vitro preserving the MPro-inhibitory activity of the boron-containing drugs. These differ from the standard conditions, which may explain why boron compounds have gone undetected in screens based on enzymatic in vitro assays. Our screening system is robust and can find inhibitors of a specific protease that are biostable, able to penetrate a cell membrane, and are not generally toxic. As a cellular assay, it can detect inhibitors that fail in a screen based on an in vitro enzymatic assay using standardized conditions, and now give support for boron compounds as MPro inhibitors. This method can also be adapted for other viral proteases.IMPORTANCEThe coronavirus disease 2019 (COVID-19) pandemic triggered the realization that we need flexible approaches to find treatments for emerging viral threats. We implemented a genetically engineered platform in yeast to detect inhibitors of the virus's main protease (MPro), a promising target to curb severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Screening molecule libraries, we identified candidate inhibitors and verified them in a biochemical assay. Moreover, the system detected boron-containing molecules as MPro inhibitors. Those were previously predicted computationally but never shown effective in a biochemical assay. Here, we demonstrate that they require a non-standard reaction buffer to function as MPro inhibitors. Hence, our cell-based method detects protease inhibitors missed by other approaches and provides support for the boron-containing molecules. We have thus demonstrated that our platform can screen large numbers of chemicals to find potential inhibitors of a viral protease. Importantly, the platform can be modified to detect protease targets from other emerging viruses.

PMID:39162260 | DOI:10.1128/spectrum.01249-24

J Neurochem. 2024 Aug 20. doi: 10.1111/jnc.16206. Online ahead of print.

ABSTRACT

Oligodendrocytes, the myelin-producing cells in the central nervous system (CNS), are crucial for rapid action potential conduction and neuronal communication. While extensively studied for their roles in neuronal support and axonal insulation, their involvement in pain modulation is an emerging research area. This review explores the interplay between oligodendrocytes, myelination, and pain, focusing on neuropathic pain following peripheral nerve injury, spinal cord injury (SCI), chemotherapy, and HIV infection. Studies indicate that a decrease in oligodendrocytes and increased cytokine production by oligodendroglia in response to injury can induce or exacerbate pain. An increase in endogenous oligodendrocyte precursor cells (OPCs) may be a compensatory response to repair damaged oligodendrocytes. Exogenous OPC transplantation shows promise in alleviating SCI-induced neuropathic pain and enhancing remyelination. Additionally, oligodendrocyte apoptosis in brain regions such as the medial prefrontal cortex is linked to opioid-induced hyperalgesia, highlighting their role in central pain mechanisms. Chemotherapeutic agents disrupt oligodendrocyte differentiation, leading to persistent pain, while HIV-associated neuropathy involves up-regulation of oligodendrocyte lineage cell markers. These findings underscore the multifaceted roles of oligodendrocytes in pain pathways, suggesting that targeting myelination processes could offer new therapeutic strategies for chronic pain management. Further research should elucidate the underlying molecular mechanisms to develop effective pain treatments.

PMID:39162089 | DOI:10.1111/jnc.16206

Endocrinology. 2024 Aug 20:bqae108. doi: 10.1210/endocr/bqae108. Online ahead of print.

NO ABSTRACT

PMID:39160063 | DOI:10.1210/endocr/bqae108

Microb Pathog. 2024 Aug 17:106863. doi: 10.1016/j.micpath.2024.106863. Online ahead of print.

ABSTRACT

Staphylococcus aureus is a bacterial pathogen that causes bloodstream infections, pneumonia, and skin abscesses and is the primary pathogen responsible for medical devices associated with biofilm infections, accounting for approximately 70% of cases. Therefore, the World Health Organization (WHO) has designated this microorganism as a top priority due to its role in causing over 20,000 bacteremia-related deaths in the US each year. The issue of pathogen resistance to antibiotics, mainly by a biofilm, further complicates these infections since biofilms render the bacterial colony impervious to antibiotics. However, many natural and synthetic substances also induce bacterial biofilm formation. Therefore, we investigated whether the most common active pharmaceutical ingredients (APIs) could induce biofilm formation in two clinical isolates of extended-spectrum beta-lactamase Staphylococcus aureus, one of them also methicillin-resistant (A2M) and two medical devices. We detected biofilm inducers, inhibitors, and destabilizers. Microbial strain, medical devices, API structure, and concentration influenced the modulatory effects of biofilm. In all devices tested, including microplates, FR18 duodenal probe, and respiratory probe, the clinic isolate methicillin-resistant S. aureus A2M exhibited lower susceptibility to biofilm formation than S. aureus A1. The anti-inflammatory acetaminophen, the hypocholesterolemic lovastatin, and the diuretic hydrochlorothiazide all induced biofilm. However, verapamil, an antihypertensive, and cetirizine, an antihistamine, inhibited biofilm on S. aureus A2M, while propranolol, another antihypertensive, inhibited biofilm on S. aureus A1. Additionally, diclofenac, an analgesic, and cetirizine destabilized the biofilm, resulting in more free bacteria and possibly making them more susceptible to external agents such as antibiotics. Nonetheless, further epidemiologic analyses and in vivo assays are needed to confirm these findings and to establish a correlation between drug use, the onset of bacterial infections in patients, and the use of medical devices. This work provides information about the probable clinical implications of drugs in patients using medical devices or undergoing surgical procedures. Inhibitory APIs could also be used as drug repurposing or templates to design new, more potent biofilm inhibitors.

PMID:39159772 | DOI:10.1016/j.micpath.2024.106863

Sci Rep. 2024 Aug 19;14(1):19133. doi: 10.1038/s41598-024-69302-w.

ABSTRACT

Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein-protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.

PMID:39160196 | DOI:10.1038/s41598-024-69302-w

Chemistry. 2024 Aug 19:e202402647. doi: 10.1002/chem.202402647. Online ahead of print.

ABSTRACT

Metals have been used in medicine for centuries. However, it was not until much later that the effects of inorganic drugs could be rationalized from a mechanistic point of view. Today, thanks to the technologies available, this approach has been functionally developed and implemented. It has been found that there is probably no single biological target for the pharmacological effects of most inorganic drugs. Herein, we present an overview of some integrated and multi-technique approaches to elucidate the molecular interactions underlying the biological effects of metallodrugs. On this premise, selected examples are used to illustrate how the information obtained on metal-based drugs and their respective mechanisms can become relevant for applications in fields other than medicine. For example, some well-known metallodrugs, which have been shown to bind specific amino acid residues of proteins, can be used to solve problems related to protein structure elucidation in crystallographic studies. Diruthenium tetraacetate can be used to catalyze the conversion of hydroxylamines to nitrones with a high selectivity when bound to lysozyme. Finally, a case study is presented in which an unprecedented palladium/arsenic-mediated catalytic cycle for nitrile hydration was discovered thanks to previous studies on the solution chemistry of the anticancer compound arsenoplatin-1 (AP-1).

PMID:39158114 | DOI:10.1002/chem.202402647

Drug Discov Today. 2024 Aug 16:104138. doi: 10.1016/j.drudis.2024.104138. Online ahead of print.

ABSTRACT

Patients diagnosed with rare diseases and their and families search desperately to organize drug discovery campaigns. Alternative models that differ from default paradigms offer real opportunities. There are, however, no clear guidelines for the development of such models, which reduces success rates and raises costs. We address the main challenges in making the discovery of new preclinical treatments more accessible, using rare hereditary paraplegia as a paradigmatic case. First, we discuss the necessary expertise, and the patients' clinical and genetic data. Then, we revisit gene therapy, de novo drug development, and drug repurposing, discussing their applicability. Moreover, we explore a pool of recommended in silico tools for pathogenic variant and protein structure prediction, virtual screening, and experimental validation methods, discussing their strengths and weaknesses. Finally, we focus on successful case applications.

PMID:39154774 | DOI:10.1016/j.drudis.2024.104138

Clin Park Relat Disord. 2024 Jul 18;11:100265. doi: 10.1016/j.prdoa.2024.100265. eCollection 2024.

ABSTRACT

This review explores the intricate connections between type 2 diabetes (T2D) and Parkinson's disease (PD), both prevalent chronic conditions that primarily affect the aging population. These diseases share common early biochemical pathways that contribute to tissue damage. This manuscript also systematically compiles potential shared cellular mechanisms between T2D and PD and discusses the literature on the utilization of antidiabetic drugs as potential therapeutic options for PD. This review encompasses studies investigating the experimental and clinical efficacy of antidiabetic drugs in the treatment of Parkinson's disease, along with the proposed mechanisms of action. The exploration of the benefits of antidiabetic drugs in PD presents a promising avenue for the treatment of this neurodegenerative disorder.

PMID:39149559 | PMC:PMC11325349 | DOI:10.1016/j.prdoa.2024.100265

bioRxiv [Preprint]. 2024 Aug 10:2023.05.12.540594. doi: 10.1101/2023.05.12.540594.

ABSTRACT

While link prediction methods in knowledge graphs have been increasingly utilized to locate potential associations between compounds and diseases, they suffer from lack of sufficient evidence to explain why a drug and a disease may be indicated. This is especially true for knowledge graph embedding (KGE) based methods where a drug-disease indication is linked only by information gleaned from a vector representation. Complementary pathwalking algorithms can increase the confidence of drug repurposing candidates by traversing a knowledge graph. However, these methods heavily weigh the relatedness of drugs, through their targets, pharmacology or shared diseases. Furthermore, these methods can rely on arbitrarily extracted paths as evidence of a compound to disease indication and lack the ability to make predictions on rare diseases. In this paper, we evaluate seven link prediction methods on a vast biomedical knowledge graph for drug repurposing. We follow the principle of consilience, and combine the reasoning paths and predictions provided by path-based reasoning approaches with those of KGE methods to identify putative drug repurposing indications. Finally, we highlight the utility of our approach through a potential repurposing indication.

PMID:39149283 | PMC:PMC11326126 | DOI:10.1101/2023.05.12.540594

medRxiv [Preprint]. 2024 Aug 7:2023.03.19.23287458. doi: 10.1101/2023.03.19.23287458.

ABSTRACT

Drug repurposing - identifying new therapeutic uses for approved drugs - is often serendipitous and opportunistic, expanding the use of drugs for new diseases. The clinical utility of drug repurposing AI models remains limited because the models focus narrowly on diseases for which some drugs already exist. Here, we introduce T x GNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, T x GNN utilizes a graph neural network and metric-learning module to rank drugs as potential indications and contraindications across 17,080 diseases. When benchmarked against eight methods, T x GNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, T x GNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form T x GNN's predictive rationales. Human evaluation of T x GNN's Explainer showed that T x GNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of T x GNN's novel predictions align with off-label prescriptions clinicians make in a large healthcare system. T x GNN's drug repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

PMID:39148855 | PMC:PMC11326339 | DOI:10.1101/2023.03.19.23287458

Nanotechnology. 2024 Aug 15. doi: 10.1088/1361-6528/ad6fa6. Online ahead of print.

ABSTRACT

Triple-negative breast cancer (TNBC) is considered the most aggressive form of breast cancer owing to the negative expression of targetable bioreceptors. Epithelial to mesenchymal transition (EMT) associated with metastatic abilities is its critical feature. As an attempt to target TNBC, nanotechnology was utilised to augment the effects of drug repurposing. Concerning that, a combination therapeutic module was structured with one of the aspects being a repurposed antihistamine, promethazine hydrochloride loaded PLGA nanoparticles. The as-synthesized nanoparticles were 217 nm in size and fluoresced at 522 nm, rendering them suitable for theranostic applications too. The second feature of the module was a common histone deacetylase inhibitor, suberoylanilide hydroxamic acid (SAHA), used as a form of pre-treatment. Experimental studies demonstrated efficient cellular internalisation and significant innate anti-proliferative potential. The use of SAHA sensitised the cells to the drug loaded nanoparticle treatment. Mechanistic studies showed increase in ROS generation, mitochondrial dysfunction followed by apoptosis. Investigations into protein expression also revealed reduction of mesenchymal proteins like vimentin by 1.90-fold; while increase in epithelial marker like E-Cadherin by 1.42-fold, thus indicating an altered EMT dynamics. Further findings also provided better insight into the benefits of SAHA potentiated targeting of tumor spheroids that mimic solid tumors of TNBC. Thus, this study paves the avenue to a more rational translational validation of combining nanotherapeutics with drug repurposing.&#xD.

PMID:39146954 | DOI:10.1088/1361-6528/ad6fa6

Sci Rep. 2024 Aug 15;14(1):18939. doi: 10.1038/s41598-024-69080-5.

ABSTRACT

Rheumatoid arthritis (RA) and arthrofibrosis (AF) are both chronic synovial hyperplasia diseases that result in joint stiffness and contractures. They shared similar symptoms and many common features in pathogenesis. Our study aims to perform a comprehensive analysis between RA and AF and identify novel drugs for clinical use. Based on the text mining approaches, we performed a correlation analysis of 12 common joint diseases including arthrofibrosis, gouty arthritis, infectious arthritis, juvenile idiopathic arthritis, osteoarthritis, post infectious arthropathies, post traumatic osteoarthritis, psoriatic arthritis, reactive arthritis, rheumatoid arthritis, septic arthritis, and transient arthritis. 5 bulk sequencing datasets and 4 single-cell sequencing datasets of RA and AF were integrated and analyzed. A novel drug repositioning method was found for drug screening, and text mining approaches were used to verify the identified drugs. RA and AF performed the highest gene similarity (0.77) and functional ontology similarity (0.84) among all 12 joint diseases. We figured out that they share the same key pathogenic cell including CD34 + sublining fibroblasts (CD34-SLF) and DKK3 + sublining fibroblasts (DKK3-SLF). Potential therapeutic target database (PTTD) was established with the differential expressed genes (DEGs) of these key pathogenic cells. Based on the PTTD, 15 potential drugs for AF and 16 potential drugs for RA were identified. This work provides a new perspective on AF and RA study which enhances our understanding of their pathogenesis. It also shed light on their underlying mechanism and open new avenues for drug repositioning studies.

PMID:39147768 | DOI:10.1038/s41598-024-69080-5