Drug Repurposing Research Articles

Revitalizing antimicrobial strategies: paromomycin and dicoumarol repurposed as potent inhibitors of M.tb's replication machinery via targeting the vital protein DnaN

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.

Regulation-aware graph learning for drug repositioning over heterogeneous biological network

Drug repositioning (DR) is crucial for identifying new disease indications for existing drugs and enhancing their clinical utility. Despite the effectiveness of various artificial intelligence techniques in discovering novel drug-disease associations (DDAs), many algorithms primarily focus on incorporating biological knowledge of drugs and diseases into DDA networks, often overlooking the rich connectivity patterns inherent in heterogeneous biological networks. In this study, we leveraged diverse connectivity patterns to gain new insights into the regulatory mechanisms of drugs acting on target proteins in diseases. We defined a set of meta-paths to reveal different regulatory mechanisms, each corresponding to distinct connectivity patterns. For each meta-path, we constructed a regulation graph through random-walk sampling of its instances in the network and obtained drug and disease embeddings through regulation-aware graph representation learning. Subsequently, we proposed a novel multi-view attention mechanism to enhance drug and disease representations. The task of predicting DDAs was accomplished using the XGBoost classifier based on the final representations of drugs and diseases. The experimental results demonstrated the superior performance of our method, RGLDR, on three benchmark datasets under ten-fold cross-validation, outperforming state-of-the-art DR algorithms across several evaluation metrics. Furthermore, case studies on two diseases indicated that RGLDR is a promising DR tool that leverages meaningful connectivity patterns for improved efficacy.

Deep learning large-scale drug discovery and repurposing.

Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing.

Anti-Ceramide ScFv Prophylaxis for First Responders to a Limited Nuclear Attack.

After 9/11, multiple government agencies instituted programs aimed at developing medical radiation countermeasures (MRCs) for two syndromes lethal within weeks of a limited nuclear attack; the hematopoietic-acute radiation syndrome (H-ARS) and the higher-dose gastrointestinal-acute radiation syndrome (GI-ARS). While re-purposing drugs that enhance marrow repopulation treats H-ARS, no mitigator protects GI tract. We recently reported anti-ceramide 6B5 single-chain variable fragment (scFv) pre-treatment abrogates ongoing small intestinal endothelial apoptosis to rescue Lgr5+ stem cells, preventing GI-ARS lethality in C57B/L6J mice. Here, with US Department of Defense support, we provide evidence that humanized anti-ceramide scFv (CX-01) is a promising prophylactic MRC for first responders, who risk exposure upon entering a radiation-contaminated site. CX-01, when delivered up to 90 min before irradiation, is highly-effective in preventing small intestinal endothelial apoptosis in mice and lethality in both sexes. Unexpectedly, females require an ~2-fold higher CX-01 dose than males for full protection. CX-01 is effective subcutaneously and intramuscularly, a property critical for battlefield use. Increasing the maximally-effective dose 5-fold does not extend duration of bioeffectiveness. While CX-01 prevents GI-ARS lethality, structural modification to extend half-life may be necessary to optimize first responder prophylaxis.

Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses

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.

How drug repurposing can advance drug discovery: challenges and opportunities

Traditional de novo drug discovery, which typically presents an 11% approval rate from phase I trials and even higher failure rates in fields like neurodegeneration, often requires two to three billion dollars and 10–17 years per new drug. In contrast, drug repurposing can reduce risks and bring drugs to the market in 3–12 years, with an average of $300 million investment. In this article, we will outline how drug repurposing can accelerate the discovery of drugs derived from natural and synthetic products. The vast amount of chemical, biological, structural, and clinical data available in public repositories will greatly facilitate drug discovery, without the need to start a discovery campaign from scratch. In the big data era, data mining and artificial intelligence will play major roles in both drug repurposing and drug discovery. This article will provide valuable insights into how drug repurposing can support drug discovery and vice versa, emphasizing its impact in addressing unmet medical needs, achieving cost-effectiveness, and enabling faster market access. Despite legal and regulatory challenges, the cost-effectiveness, and the potential to give new life to compounds already in the pipeline make drug repurposing a crucial complement to traditional drug discovery in the era of precision medicine.

Drug repositioning for rosacea disease: Biological TARGET identification, molecular docking, pharmacophore mapping, and molecular dynamics analysis

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.

Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications

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.

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.

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.

Integrated transcriptomics- and structure-based drug repositioning identifies drugs with proteasome inhibitor properties

Computational pharmacogenomics can potentially identify new indications for already approved drugs and pinpoint compounds with similar mechanism-of-action. Here, we used an integrated drug repositioning approach based on transcriptomics data and structure-based virtual screening to identify compounds with gene signatures similar to three known proteasome inhibitors (PIs; bortezomib, MG-132, and MLN-2238). In vitro validation of candidate compounds was then performed to assess proteasomal proteolytic activity, accumulation of ubiquitinated proteins, cell viability, and drug-induced expression in A375 melanoma and MCF7 breast cancer cells. Using this approach, we identified six compounds with PI properties ((-)-kinetin-riboside, manumycin-A, puromycin dihydrochloride, resistomycin, tegaserod maleate, and thapsigargin). Although the docking scores pinpointed their ability to bind to the β5 subunit, our in vitro study revealed that these compounds inhibited the β1, β2, and β5 catalytic sites to some extent. As shown with bortezomib, only manumycin-A, puromycin dihydrochloride, and tegaserod maleate resulted in excessive accumulation of ubiquitinated proteins and elevated HMOX1 expression. Taken together, our integrated drug repositioning approach and subsequent in vitro validation studies identified six compounds demonstrating properties similar to proteasome inhibitors.

Computational analysis of pathogen-host interactome for fast and low-risk in-silico drug repurposing in emerging viral threats like Mpox

Monkeypox (Mpox), a zoonotic illness triggered by the monkeypox virus (MPXV), poses a significant threat since it may be transmitted and has no cure. This work introduces a computational method to predict Protein–Protein Interactions (PPIs) during MPXV infection. The objective is to discover prospective drug targets and repurpose current potential Food and Drug Administration (FDA) drugs for therapeutic purposes. In this work, ensemble features, comprising 2–5 node graphlet attributes and protein composition-based features are utilized for Deep Learning (DL) models to predict PPIs. The technique that is used here demonstrated an excellent prediction performance for PPI on both the Human Integrated Protein–Protein Interaction Reference (HIPPIE) and MPXV-Human PPI datasets. In addition, the human protein targets for MPXV have been identified accurately along with the detection of possible therapeutic targets. Furthermore, the validation process included conducting docking research studies on potential FDA drugs like Nicotinamide Adenine Dinucleotide and Hydrogen (NADH), Fostamatinib, Glutamic acid, Cannabidiol, Copper, and Zinc in DrugBank identified via research on drug repurposing and the Drug Consensus Score (DCS) for MPXV. This has been achieved by employing the primary crystal structures of MPXV, which are now accessible. The docking study is also supported by Molecular Dynamics (MD) simulation. The results of our study emphasize the effectiveness of using ensemble feature-based PPI prediction to understand the molecular processes involved in viral infection and to aid in the development of repurposed drugs for emerging infectious diseases such as, but not limited to, Mpox. The source code and link to data used in this work is available at: https://github.com/CMATERJU-BIOINFO/In-Silico-Drug-Repurposing-Methodology-To-Suggest-Therapies-For-Emerging-Threats-like-Mpox.

Drug Repurposing using consilience of Knowledge Graph Completion methods.

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.

Multi-omics analysis identifies repurposing bortezomib in the treatment of kidney-, nervous system-, and hematological cancers

Repurposing of FDA-approved drugs is a quick and cost-effective alternative to de novo drug development. Here, we identify genes involved in bortezomib sensitivity, predict cancer types that may benefit from treatment with bortezomib, and evaluate the mechanism-of-action of bortezomib in breast cancer (BT-474 and ZR-75–30), melanoma (A-375), and glioblastoma (A-172) cells in vitro. Cancer cell lines derived from cancers of the blood, kidney, nervous system, and skin were found to be significantly more sensitive to bortezomib than other organ systems. The in vitro studies confirmed that although bortezomib effectively inhibited the β5 catalytic site in all four cell lines, cell cycle arrest was only induced in G2/M phase and apoptosis in A-375 and A-172 after 24h. The genomic and transcriptomic analyses identified 33 genes (e.g. ALDH18A1, ATAD2) associated with bortezomib resistance. Taken together, we identified biomarkers predictive of bortezomib sensitivity and cancer types that might benefit from treatment with bortezomib.

Surface Modified Glucose‐Derived, Blood–Brain Barrier‐Crossing Nanospheres Dually Targets Macrophage and Cancer Cells for Effective In Situ Anti‐Glioma Effect

AbstractGlucose‐derived carbon nanospheres (CSP), uniquely derived by hydrothermal condensation process, inherently cross blood–brain‐barrier (BBB) but distribute all over the brain. Albeit its potential to treat glioma as an effective drug delivery system, it is challenging to restrict drug‐associated CSP within the glioma region and reduce non‐specific side effects. Incidentally, gliomas moderately express sigma receptors (SR). Earlier, a cationic lipid‐conjugated neuropsychotic drug, haloperidol (H8) is developed with SR‐targetability and anticancer effect but with zero BBB‐crossing ability. In this study, the CSP surface is modified with H8 (CH8 nano‐conjugate) and dual targeting is achieved within glioma‐tumor microenvironment: 1) glioma cells and 2) pro‐proliferative M2 tumor‐associated macrophages (TAM), as both express SR. CH8‐treatment increases the survivability of orthotopic glioma‐tumor bearing mice and significantly reduces tumor burden in the glioma‐subcutaneous model. Further CH8‐surface is modified by combining the brain tumor drug, carmustine (CH8‐CRM). CH8‐CRM nano‐conjugate selectively enhances the survivability of orthotopic glioma‐carrying mice and reduces tumor aggressiveness significantly in comparison to other treatment groups. Lysates from CH8‐CRM‐treated tumor show upregulation of cleaved‐caspase 3, p53, but downregulation of pAkt. The combination treatment pronouncedly enhances the anti‐glioma effect of H8. Conclusively, CH8‐mediated dual‐targeting via SR within orthotopic glioma‐associated mice exemplifies the repurposing of neuropsychotic drugs for treating glioma.

Unveiling the potency of FDA-approved oxidopamine HBr for cervical cancer regulation and replication proteins.

Cervical Cancer remains a women's health concern worldwide and ranks among the most prevalent cancers, particularly in developing countries. Many women are diagnosed with cervical cancer, with a substantial number succumbing to the disease even after the availability of vaccines and drugs. The tumour microenvironment often exhibits immune evasion, including suppression of T-cell activity and altered cytokine, impacting the efficacy of therapeutic interventions and highlighting the need for treatments to modulate the immune response. Despite efforts to promote HPV vaccination and regular screenings, it causes many deaths, underscoring the urgent need for continued research, healthcare access, and rapid drug development or repurposing. In this study, we identified various proteins involved in cervical cancer cell cycle regulation and DNA replication proteins, performed the multitargeted docking with an FDA-approved library, and identified Oxidopamine HBr as a multitargeted drug. Studies extended with pharmacokinetics and compared with the standard values followed by DFT, which supported the compound as a multitargeted inhibitor. Further, the docked complexes were taken for the interaction fingerprints, and it was identified that there are many 9 polar, 5 hydrophobic, 2 aromatic, and 2 basic residues. We extended our studies for 100ns MD Simulation in water, and the computations explored the deviation and fluctuations under 2Å and many intermolecular interactions; the same trajectory files were used for the MM\GBSA studies. All the studies have supported the Oxidopamine HBr as a cervical cancer multitargeted inhibitor-however, experimental studies are needed before human use.

A foundation model for clinician-centered drug repurposing.

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 TXGNN, 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, TXGNN 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, TXGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TXGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TXGNN's predictive rationales. Human evaluation of TXGNN's Explainer showed that TXGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's novel predictions align with off-label prescriptions clinicians make in a large healthcare system. TXGNN's drug repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

Genome-wide assessment of shared genetic landscape of idiopathic pulmonary fibrosis and its comorbidities

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease accompanied by both local and systemic comorbidities. Genetic factors play a role in the development of IPF and certain associated comorbidities. Nevertheless, it is uncertain whether there are shared genetic factors underlying IPF and these comorbidities. To bridge this knowledge gap, we conducted a systematic investigation into the shared genetic architecture between IPF and ten prevalent heritable comorbidities (i.e., body mass index [BMI], coronary artery disease [CAD], chronic obstructive pulmonary disease [COPD], gastroesophageal reflux disease, lung cancer, major depressive disorder [MDD], obstructive sleep apnoea, pulmonary hypertension [PH], stroke, and type 2 diabetes), by utilizing large-scale summary data from their respective genome-wide association studies and multi-omics studies. We revealed significant (false discovery rate [FDR] < 0.05) and moderate genetic correlations between IPF and seven comorbidities, excluding lung cancer, MDD and PH. Evidence suggested a partially putative causal effect of IPF on CAD. Notably, we observed FDR-significant genetic enrichments in lung for the cross-trait between IPF and CAD and in liver for the cross-trait between IPF and COPD. Additionally, we identified 65 FDR-significant genes over-represented in 20 biological pathways related to the etiology of IPF, BMI, and COPD, including inflammation-related mucin gene clusters. Several of these genes were associated with clinically relevant drugs for the treatment of IPF, CAD, and/or COPD. Our results underscore the pervasive shared genetic basis between IPF and its common comorbidities and hold future implications for early diagnosis of IPF-related comorbidities, drug repurposing, and the development of novel therapies for IPF.

Integrative Analysis of Multi-Omics Data to Identify Deregulated Molecular Pathways and Druggable Targets in Chronic Lymphocytic Leukemia.

Chronic Lymphocytic Leukemia (CLL) is the most common B-cell malignancy in the Western world, characterized by frequent relapses despite temporary remissions. Our study integrated publicly available proteomic, transcriptomic, and patient survival datasets to identify key differences between healthy and CLL samples. We exposed approximately 1000 proteins that differentiate healthy from cancerous cells, with 608 upregulated and 415 downregulated in CLL cases. Notable upregulated proteins include YEATS2 (an epigenetic regulator), PIGR (Polymeric immunoglobulin receptor), and SNRPA (a splicing factor), which may serve as prognostic biomarkers for this disease. Key pathways implicated in CLL progression involve RNA processing, stress resistance, and immune response deficits. Furthermore, we identified three existing drugs-Bosutinib, Vorinostat, and Panobinostat-for potential further investigation in drug repurposing in CLL. We also found limited correlation between transcriptomic and proteomic data, emphasizing the importance of proteomics in understanding gene expression regulation mechanisms. This generally known disparity highlights once again that mRNA levels do not accurately predict protein abundance due to many regulatory factors, such as protein degradation, post-transcriptional modifications, and differing rates of translation. These results demonstrate the value of integrating omics data to uncover deregulated proteins and pathways in cancer and suggest new therapeutic avenues for CLL.

The Anticancer Activity of Monosaccharides: Perspectives and Outlooks.

A major hallmark of cancer is the reprogramming of cellular metabolism from oxidative phosphorylation (OXPHOS) to glycolysis, a phenomenon known as the Warburg effect. To sustain high rates of glycolysis, cancer cells overexpress GLUT transporters and glycolytic enzymes, allowing for the enhanced uptake and consumption of glucose. The Warburg effect may be exploited in the treatment of cancer; certain epimers and derivatives of glucose can enter cancer cells and inhibit glycolytic enzymes, stunting metabolism and causing cell death. These include common dietary monosaccharides (ᴅ-mannose, ᴅ-galactose, ᴅ-glucosamine, ʟ-fucose), as well as some rare monosaccharides (xylitol, ᴅ-allose, ʟ-sorbose, ʟ-rhamnose). This article reviews the literature on these sugars in in vitro and in vivo models of cancer, discussing their mechanisms of cytotoxicity. In addition to this, the anticancer potential of some synthetically modified monosaccharides, such as 2-deoxy-ᴅ-glucose and its acetylated and halogenated derivatives, is reviewed. Further, this article reviews how certain monosaccharides can be used in combination with anticancer drugs to potentiate conventional chemotherapies and to help overcome chemoresistance. Finally, the limitations of administering two separate agents, a sugar and a chemotherapeutic drug, are discussed. The potential of the glycoconjugation of classical or repurposed chemotherapy drugs as a solution to these limitations is reviewed.

Mitochondrial signaling pathways and their role in cancer drug resistance

Mitochondria, traditionally known as cellular powerhouses, now emerge as critical signaling centers influencing cancer progression and drug resistance. The review highlights the role that apoptotic signaling, DNA mutations, mitochondrial dynamics and metabolism play in the development of resistance mechanisms and the advancement of cancer. Targeted approaches are discussed, with an emphasis on managing mitophagy, fusion, and fission of the mitochondria to make resistant cancer cells more susceptible to traditional treatments. Additionally, metabolic reprogramming can be used to effectively target metabolic enzymes such GLUT1, HKII, PDK, and PKM2 in order to avoid resistance mechanisms. Although there are potential possibilities for therapy, the complex structure of mitochondria and their subtle role in tumor development hamper clinical translation. Novel targeted medicines are put forth, providing fresh insights on combating drug resistance in cancer. The study also emphasizes the significance of glutamine metabolism, mitochondrial respiratory complexes, and apoptotic pathways as potential targets to improve treatment effectiveness against drug-resistant cancers. Combining complementary and nanoparticle-based techniques to target mitochondria has demonstrated encouraging results in the treatment of cancer, opening doors to reduce resistance and enable individualized treatment plans catered to the unique characteristics of each patient. Suggesting innovative approaches such as drug repositioning and mitochondrial drug delivery to enhance the efficacy of mitochondria-targeting therapies, presenting a pathway for advancements in cancer treatment. This thorough investigation is a major step forward in the treatment of cancer and has the potential to influence clinical practice and enhance patient outcomes.