Candidates For Drug Repurposing Research Articles

Drug repurposing for glomerular diseases: an underutilized resource.

Drug repurposing in glomerular disease can deliver opportunities for steroid-free regimens, enable personalized multi-target options for resistant or relapsing disease and enhance treatment options for understudied populations (for example, children) and in resource-limited settings. Identification of drug-repurposing candidates can be data driven, which utilizes existing data on disease pathobiology, drug features and clinical outcomes, or experimental, which involves high-throughput drug screens. Information from databases of approved drugs, clinical trials and PubMed registries suggests that at least 96 drugs on the market cover 49 targets with immunosuppressive potential that could be candidates for drug repurposing in glomerular disease. Furthermore, evidence to support drug repurposing is available for 191 immune drug target-glomerular disease pairs. Non-immunological drug repurposing includes strategies to reduce haemodynamic overload, podocyte injury and kidney fibrosis. Recommended strategies to expand drug-repurposing capacity in glomerular disease include enriching drug databases with glomeruli-specific information, enhancing the accessibility of primary clinical trial data, biomarker discovery to improve participant selection into clinical trials and improve surrogate outcomes and initiatives to reduce patent, regulatory and organizational hurdles.

In vitro synergistic antiviral activity of repurposed drugs against enterovirus 71.

Enteroviruses cause viral diseases that are harmful to children. Hand, foot, and mouth disease (HFMD) with neurological complications is mainly caused by enterovirus 71 (EV71). Despite its clinical importance, there is no effective antiviral drug against EV71. However, several repurposed drugs have been shown to have antiviral activity against related viruses. Treatments with single drugs and two-drug combinations were performed in vitro to assess anti-EV71 activity. Three repurposed drug candidates with broad-spectrum antiviral activity were found to demonstrate potent anti-EV71 activity: prochlorperazine, niclosamide, and itraconazole. To improve antiviral activity, combinations of two drugs were tested. Niclosamide and itraconazole showed synergistic antiviral activity in Vero cells, whereas combinations of niclosamide-prochlorperazine and itraconazole-prochlorperazine showed only additive effects. Furthermore, the combination of itraconazole and prochlorperazine showed an additive effect in neuroblastoma cells. Itraconazole and prochlorperazine exert their antiviral activities by inhibiting Akt phosphorylation. Repurposing of drugs can provide a treatment solution for HFMD, and our data suggest that combining these drugs can enhance that efficacy.

Identification of Parkinson’s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data

Parkinson’s disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals’ phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

Identification of Parkinson’s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data

Parkinson’s disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals’ phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates.

Current effective breast cancer treatment options have severe side effects, highlighting a need for new therapies. Drug repurposing can accelerate improvements to care, as FDA-approved drugs have known safety and pharmacological profiles. Some drugs for other conditions, such as metformin, an antidiabetic, have been tested in clinical trials for repurposing for breast cancer. Here, we exploit the genetics of breast cancer and linked predisposing diseases to propose novel drug repurposing. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drug targets, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drug repurposing, but also to highlight shared etiology explaining repurposing. We compile breast cancer's predisposing diseases from literature. For each predisposing disease, we use GWAS summary statistics to identify genes in loci showing genetic correlation with breast cancer. Then, we use a network approach to link these shared genes to canonical pathways, and similarly for all drugs treating the predisposing disease, we link their targets to pathways. In this manner, we are able to prioritize a list of drugs based on each predisposing disease, with each drug linked to a set of implicating pathways. Finally, we evaluate our recommendations against drugs currently under investigation for breast cancer. We identify 84 loci harboring mutations with positively correlated effects between breast cancer and its predisposing diseases; these contain 194 identified shared genes. Out of the 112 drugs indicated for the predisposing diseases, 76 drugs can be linked to shared genes via pathways (candidate drugs for repurposing). Fifteen out of these candidate drugs are already in advanced clinical trial phases or approved for breast cancer (OR = 9.28, p = 7.99e-03, one-sided Fisher's exact test), highlighting the ability of our approach to identify likely successful candidate drugs for repurposing. Our novel approach accelerates drug repurposing for breast cancer by leveraging shared genetics with its known risk factors. The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.

Antidepressant-induced membrane trafficking regulates blood-brain barrier permeability.

As the most prescribed psychotropic drugs in current medical practice, antidepressant drugs (ADs) of the selective serotonin reuptake inhibitor (SSRI) class represent prime candidates for drug repurposing. The mechanisms underlying their mode of action, however, remain unclear. Here, we show that common SSRIs and selected representatives of other AD classes bidirectionally regulate fluid-phase uptake at therapeutic concentrations and below. We further characterize membrane trafficking induced by a canonical SSRI fluvoxamine to show that it involves enhancement of clathrin-mediated endocytosis, endosomal system, and exocytosis. RNA sequencing analysis showed few fluvoxamine-associated differences, consistent with the effect being independent of gene expression. Fluvoxamine-induced increase in membrane trafficking boosted transcytosis in cell-based blood-brain barrier models, while a single injection of fluvoxamine was sufficient to enable brain accumulation of a fluid-phase fluorescent tracer in vivo. These findings reveal modulation of membrane trafficking by ADs as a possible cellular mechanism of action and indicate their clinical repositioning potential for regulating drug delivery to the brain.

DReAmocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing.

In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.

Clofarabine Has a Superior Therapeutic Window as compared to Gemcitabine in Preclinical Bladder Cancer Models

Current standard-of-care systemic therapy options for locally advanced and metastatic bladder cancer (BC), which are predominantly based on cisplatin-gemcitabine combinations, are limited by significant treatment failure rates and frailty-based patient ineligibility. We previously addressed the urgent clinical need for better-tolerated BC therapeutic strategies using a drug screening approach, which identified outstanding antineoplastic activity of clofarabine in preclinical models of BC. To further assess clofarabine as a potential BC therapy component, we conducted head-to-head comparisons of responses to clofarabine versus gemcitabine in preclinical in vitro and in vivo models of BC, complemented by in silico analyses. In vitro data suggest a distinct correlation between the two antimetabolites, with higher cytotoxicity of gemcitabine, especially against several nonmalignant cell types, including keratinocytes and endothelial cells. Accordingly, tolerance of clofarabine (oral or intraperitoneal application) was distinctly better than for gemcitabine (intraperitoneal) in patient-derived xenograft models of BC. Clofarabine also exhibited distinctly superior anticancer efficacy, even at dosing regimens optimized for gemcitabine. Neither complete remission nor cure, both of which were observed with clofarabine, were achieved with any tolerable gemcitabine regimen. Taken together, our findings demonstrate that clofarabine has a better therapeutic window than gemcitabine, further emphasizing its potential as a candidate for drug repurposing in BC. Patient summaryWe compared the anticancer activity of clofarabine, a drug used for treatment of leukemia but not bladder cancer, and gemcitabine, a drug currently used for chemotherapy against bladder cancer. Using cell cultures and mouse models, we found that clofarabine was better tolerated and more efficacious than gemcitabine, and even cured implanted tumors in mouse models. Our results suggest that clofarabine, alone or in combination schemes, might be superior to gemcitabine for the treatment of bladder cancer.

Using word evolution to predict drug repurposing

BackgroundTraditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing.ResultsWord embeddings, which represent a word’s context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug’s repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE.ConclusionsThe annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique’s suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

Abstract 6150: MMP14 and MKLN1 as colorectal cancer susceptibility genes and a drug repurposing candidate from genome-wide association study in South Korea

Abstract Background: One of the major topics about colorectal cancer (CRC) is to find susceptible genes. Furthermore, functional interpretation of post-GWAS analysis gains attention and various approaches to find drug-repurposing candidates are used in cancer research. Our study aims to identify susceptible genes for CRC and provide drug-repurposing candidates. Methods: CRC patients in Seoul National University Hospital with prospective follow-up were recruited. From blood-derived DNA, genome-wide SNPs were genotyped using the Korea Biobank Array and 409,063 SNPs were extracted. SNP-based logistic regression model was fit for the event of CRC estimating beta values. Furthermore, post-GWAS analysis was conducted using regulatory function, multiple annotation and gene expression database. Drug-repurposing candidate was identified through the pre-trained deep neural network with susceptible genes. Results: We used 500 participants of CRC cases and 1,500 participants of healthy controls. For GWAS, statistically significant associations were mapped to MKLN1, MMP14, PTPN14 and MKL2 genes. For post-GWAS analysis, strong associations were found for MKLN1 (rs75170436, 7q32.3, Beta=-0.90, P-value=5.90×10-13) and MMP14 (rs3751489, 14q11.2, Beta=-1.91, P-value=2.31×10-12). From the drug-repurposing analysis, PPARδ/β agonist (GW0742; Binding score=3.79(MKLN1), 12.06(MMP14)) was identified for both genes with the highest score. Conclusions: We revealed MKLN1 and MMP14 genes as susceptible genes associated with CRC development. Additionally, we identified PPARδ/β agonist (GW0742) as a drug-repurposing candidate for the treatment of CRC. These findings can promote the understanding of CRC development and provide a novel therapeutic candidate for CRC patients in the future. Citation Format: Dabin Yun, Jung-Ho Yang, Sun-Seong Kweon, Jin-ah Sim, Minjung Kim, Ji Won Park, Seung Yong Jeong, Aesun Shin, Nan Song. MMP14 and MKLN1 as colorectal cancer susceptibility genes and a drug repurposing candidate from genome-wide association study in South Korea [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6150.

Nortriptyline hydrochloride, a potential candidate for drug repurposing, inhibits gastric cancer by inducing oxidative stress by triggering the Keap1-Nrf2 pathway

Effective drugs for the treatment of gastric cancer (GC) are still lacking. Nortriptyline Hydrochloride (NTP), a commonly used antidepressant medication, has been demonstrated by numerous studies to have antitumor effects. This study first validated the ability of NTP to inhibit GC and preliminarily explored its underlying mechanism. To begin with, NTP inhibits the activity of AGS and HGC27 cells (Human-derived GC cells) in a dose-dependent manner, as well as proliferation, cell cycle, and migration. Moreover, NTP induces cell apoptosis by upregulating BAX, BAD, and c-PARP and downregulating PARP and Bcl-2 expression. Furthermore, the mechanism of cell death caused by NTP is closely related to oxidative stress. NTP increases intracellular reactive oxygen species (ROS) and malondialdehyde (MDA) levels, decreasing the mitochondrial membrane potential (MMP) and inducing glucose (GSH) consumption. While the death of GC cells can be partially rescued by ROS inhibitor N-acetylcysteine (NAC). Mechanistically, NTP activates the Kelch-like ECH-associated protein (Keap1)—NF-E2-related factor 2 (Nrf2) pathway, which is an important pathway involved in oxidative stress. RNA sequencing and proteomics analysis further revealed molecular changes at the mRNA and protein levels and provided potential targets and pathways through differential gene expression analysis. In addition, NTP can inhibited tumor growth in nude mouse subcutaneous tumor models constructed respectively using AGS and MFC (mouse-derived GC cells), providing preliminary evidence of its effectiveness in vivo. In conclusion, our study demonstrated that NTP exhibits significant anti-GC activity and is anticipated to be a candidate for drug repurposing.

Pharmacokinetic considerations for enhancing drug repurposing opportunities of anthelmintics: Niclosamide as a case study

Recently, anthelmintics have showcased versatile therapeutic potential in addressing various diseases, positioning them as promising candidates for drug repurposing. However, challenges such as low bioavailability and a lack of a solid pharmacokinetic basis impede successful repurposing. To overcome these flaws, we aimed to investigate the key pharmacokinetic factors of anthelmintics mainly focusing on the absorption, distribution, and metabolism profiles by employing niclosamide (NIC) as a model drug. The intestinal permeability of NIC is significantly influenced by solubility and doesn't function as a substrate for efflux transporters. It showed high plasma protein binding. Also, the metabolism study indicated that NIC would have low metabolic stability by extensively undergoing the intestinal glucuronidation. Additionally, we investigated the CYP-mediated drug-drug interaction potential of NIC in both direct and time-dependent ways. NIC showed strong inhibitory effects on CYP1A2 and CYP2C8 and is not likely to become a time-dependent inhibitor. Our findings could contribute to the identification of essential factors in the pharmacokinetics of anthelmintics, potentially facilitating their repositioning.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein’s crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

Identification of Repurposable Drugs for Colorectal Cancer Using Drug-Network-Based Classification Models

Colorectal cancer (CRC) is the second most lethal cancer with more than one million new cases diagnosed worldwide every year. To defuse the increasing CRC threat, more effective and less harmful treatments for CRC patients are urgently needed. Computational drug repurposing, which is an in silico based approach to uncover new indications of approved drugs, is a promising strategy to accelerate the time to market of drugs. However, there are not many computational drug repurposing methods for CRC. In this work, we proposed drug-network-based classification models to identify repurposable drugs for CRC. Initially, four drug networks, the chemical structure network (CSN), the target protein network (TPN), the drug pathway network (PWN), and the drug-drug interaction network (DIN), were formulated. Based on the drug features properly extracted from the networks, we created four multi-layer perceptron (MLP) models. By comparing the performance of the models, the DIN model outperformed the others with the highest accuracy and an F1 score of 96.9%. After predicting the repurposability of over 1,200 non-CRC approved drugs using the DIN model, 306 drugs discovered as potentially repurposable drugs for CRC. In summary, the drug-network-based classification models can efficiently identify repurposable drug candidates for CRC, which would be applicable for efficient therapeutic treatment of CRC.

Repurposed drugs in combinations exert additive anti-chikungunya virus activity: an in-vitro study

Chikungunya virus (CHIKV) infection causes chikungunya, a viral disease that currently has no specific antiviral treatment. Several repurposed drug candidates have been investigated for the treatment of the disease. In order to improve the efficacy of the known drugs, combining drugs for treatment is a promising approach. The current study was undertaken to explore the antiviral activity of a combination of repurposed drugs that were reported to have anti-CHIKV activity. We explored the effect of different combinations of six effective drugs (2-fluoroadenine, emetine, lomibuvir, enalaprilat, metyrapone and resveratrol) at their non-toxic concentrations against CHIKV under post infection treatment conditions in Vero cells. Focus-forming unit assay, real time RT-PCR, immunofluorescence assay, and western blot were used to determine the virus titre. The results revealed that the combination of 2-fluoroadenine with either metyrapone or emetine or enalaprilat exerted inhibitory activity against CHIKV under post-infection treatment conditions. The effect of these drug combinations was additive in nature compared to the effect of the individual drugs. The results suggest an additive anti-viral effect of these drug combinations against CHIKV. The findings could serve as an outline for the development of an innovative therapeutic approach in the future to treat CHIKV-infected patients.

Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses

BackgroundDelirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment.MethodsWe used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database.ResultsWe have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments.ConclusionThis study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.

DDDR-24. SINGLE-CELL FUNCTIONAL TISSUE PROFILING IDENTIFIES PROMISING CANDIDATES FOR DRUG REPURPOSING AGAINST GLIOBLASTOMA

Abstract There remains an unmet need for novel treatment strategies against glioblastoma that are more effective than the current standard of care. However, the complex tumor microenvironment and genetic heterogeneity of these tumors require novel technologies enabling high-throughput assessment of cellular responses to treatments on a functional level in complex tumor samples with an intact cellular microenvironment. Here, we used a novel single-cell functional tissue profiling platform based on high—throughput microscopy followed by computer vision and machine learning algorithms to screen 70 clinically-approved drugs with good blood-brain-barrier penetration in 35 glioblastoma patient samples that were freshly obtained from surgery. This revealed prognostically relevant subtypes of glioma cell morphologies and promising candidates for drug repurposing. Among the drugs with potent anti-glioma activity, we identified the antidepressant Vortioxetine, that had strong anti-glioma activity across patients independent of the promoter methylation status of O6-methylguanine-DNA methyltransferase (MGMT). Complementrary in vitro studies using patient-derived glioma cell lines and orthotopic glioma xenograft mouse models confirmed the strong anti-glioma activity of Vortioxetine, leading to a notable fraction of long-term surviving mice. Mechanistically, multiplexed transcriptomics and proteomics unveiled that Vortioxetine induced an upregulation of the Activator protein 1 (AP-1) transcription factor family, along with the B-cell translocation gene (BTG) tumor suppressor genes. These findings serve as the cornerstone for future clinical applications of the single-cell functional tissue profiling platform and the potential clinical translation of Vortioxetine as a promising drug-repurposing candidate for glioblastoma patients.

Single-Cell Meta-Analysis of Neutrophil Activation in Kawasaki Disease and Multisystem Inflammatory Syndrome in Children Reveals Potential Shared Immunological Drivers.

Kawasaki disease (KD) and multisystem inflammatory syndrome in children (MIS-C) share similar clinical manifestations, including cardiovascular complications, suggesting similar underlying immunopathogenic processes. Aberrant neutrophil activation may play a crucial role in the shared pathologies of KD and MIS-C; however, the associated pathogenic mechanisms and molecular drivers remain unknown. We performed a single-cell meta-analysis of neutrophil activation with 103 pediatric single-cell transcriptomic peripheral blood mononuclear cell data across 9 cohorts, including healthy controls, KD, MIS-C, compared with dengue virus infection, juvenile idiopathic arthritis, and pediatric celiac disease. We used a series of computational analyses to investigate the shared neutrophil transcriptional programs of KD and MIS-C that are linked to systemic damage and cardiac pathologies, and suggested Food and Drug Administration-approved drugs to consider as KD and MIS-C treatment. We meta-analyzed 521 950 high-quality cells. We found that blood signatures associated with risks of cardiovascular events are enriched in neutrophils of KD and MIS-C. We revealed the expansion of CD177+ neutrophils harboring hyperactivated effector functions in both KD and MIS-C, but not in healthy controls or in other viral-, inflammatory-, or immune-related pediatric diseases. KD and MIS-C CD177+ neutrophils had highly similar transcriptomes, marked by conserved signatures and pathways related to molecular damage. We found the induction of a shared neutrophil expression program, potentially regulated by SPI1 (Spi-1 proto-oncogene), which confers enhanced effector functions, especially neutrophil degranulation. CD177 and shared neutrophil expression program expressions were associated with acute stages and attenuated during KD intravenous immunoglobulin treatment and MIS-C recovery. Network analysis identified hub genes that correlated with the high activation of CD177+ neutrophils. Disease-gene association analysis revealed that the KD and MIS-C CD177+ neutrophils' shared expression program was associated with the development of coronary and myocardial disorders. Last, we identified and validated TSPO (translocator protein) and S100A12 (S100 calcium-binding protein A12) as main molecular targets, for which the Food and Drug Administration-approved drugs methotrexate, zaleplon, metronidazole, lorazepam, clonazepam, temazepam, and zolpidem, among others, are primary candidates for drug repurposing. Our findings indicate that CD177+ neutrophils may exert systemic pathological damage contributing to the shared morbidities in KD and MIS-C. We uncovered potential regulatory drivers of CD177+ neutrophil hyperactivation and pathogenicity that may be targeted as a single therapeutic strategy for either KD or MIS-C.

Decoding Systems Biology of Inflammation Signatures in Cancer Pathogenesis: Pan-Cancer Insights from 12 Common Cancers.

Chronic inflammation is an important contributor to tumorigenesis in many tissues. However, the underlying mechanisms of inflammatory signaling in the tumor microenvironment are not yet fully understood in various cancers. Therefore, this study aimed to uncover the gene expression signatures of inflammation-associated proteins that lead to tumorigenesis, and with an eye to discovery of potential system biomarkers and novel drug candidates in oncology. Gene expression profiles associated with 12 common cancers (e.g., breast invasive carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma) from The Cancer Genome Atlas were retrieved and mapped to inflammation-related gene sets. Subsequently, the inflammation-associated differentially expressed genes (i-DEGs) were determined. The i-DEGs common in all cancers were proposed as tumor inflammation signatures (TIS) after pan-cancer analysis. A TIS, consisting of 45 proteins, was evaluated as a potential system biomarker based on its prognostic forecasting and secretion profiles in multiple tissues. In addition, i-DEGs for each cancer type were used as queries for drug repurposing. Narciclasine, parthenolide, and homoharringtonine were identified as potential candidates for drug repurposing. Biomarker candidates in relation to inflammation were identified such as KNG1, SPP1, and MIF. Collectively, these findings inform precision diagnostics development to distinguish individual cancer types, and can also pave the way for novel prognostic decision tools and repurposed drugs across multiple cancers. These new findings and hypotheses warrant further research toward precision/personalized medicine in oncology. Pan-cancer analysis of inflammatory mediators can open up new avenues for innovation in cancer diagnostics and therapeutics.

Drug Repurposing Analysis for Colorectal Cancer through Network Medicine Framework: Novel Candidate Drugs and Small Molecules

This study aimed to reveal the drug-repurposing candidates for colorectal cancer (CRC) via drug-repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol (PEG), gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.