Drug Repurposing Research Articles

Abstract 3503: New generation drug development from in-silico identification to preclinical evaluation in glioblastoma

Abstract Glioblastoma(GBM) is the most common malignancy of the central nervous system in adults. Despite medical advancements, stagnant survival rate improvements (<13%) and failure of conventional treatments such as temozolomide, have warranted the need for novel drug discovery methodologies. Considering the enormous potential of drug repurposing, we developed a method to identify a safe, active, and effective treatment for GBM. This method comprises two key stages, drug-target prediction and preclinical functional evaluation. Stage 1 fabricated a novel five phase in-silico pipeline for drug-target prediction against Ephrin-Type-A Receptor 2, chosen through preliminary literature and genomic analysis. Around 1562 FDA approved drugs were screened against the developed criterion and analyzed for bond type, location, repulsion, Gibbs free energy, stability, interactions over time, blood brain barrier permeability, and possible false positives (PAINS). Our results provided five plausible hits: Empagliflozin, Mycophenolate mofetil, Canagliflozin, Rivaroxaban, and Carvedilol. We subsequently evaluated the anti-proliferative effects of these hits using Sulforhodamine-B cytotoxicity assay on glioblastoma cell lines U251 and SF295. Our results demonstrated that Canaglifozin and Empagliflozin reduced the proliferation of GBM cell lines with IC50 value ranging from 30-40 μM. On the other hand, Mycophenolate mofetil (IC50: 5μM) and Carvedilol (IC50:16μM) also exhibited significant cytotoxic effects. Effects of Mycophenolate mofetil and Carvedilol were further evaluated in pancreatic cell line SUIT-2 where results exhibited cytotoxic responses similar to those observed in GBM. Furthermore, Annexin V/FITC analysis showed apoptosis in SUIT-2 cells by Carvedilol. Taken together, our results show the anti-cancer repurposing candidates extracted from our in-silico pipeline show significant cytotoxic effects of compounds identified through in-silico screening. Further studies are currently underway in our laboratory to delineate the anti-cancer mechanism, toxicity and off-target effects of these compounds. Citation Format: Mahesh Arunachalam, Shreyas Gaikwad, Sharavan Ramachandran, Sanjay K. Srivastava. New generation drug development from in-silico identification to preclinical evaluation in glioblastoma [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 3503.

Abstract 938: Screening the entire kinase-directed, FDA-approved pharmacopeia against the largest collection of wild-type and mutant kinases reveals many opportunities for drug repurposing and targeted therapy

Abstract We sought to screen all FDA approved, cancer-indicated, kinase targeting drugs using the gold standard radiometric biochemical activity assay (HotSpot) against the largest collection of kinases in the world (>100 drugs against 735 total kinases including 395 wild type and 340 mutants). Virtually every drug examined exhibits polypharmacology inhibiting many kinases. Rarely does the intended target represent the greatest inhibition observed from a drug. Furthermore, most drugs inhibit a select group of mutant kinases, including especially mutants of kinases distinct from the intended target. Within a given kinase, each cancer-identified mutation is often as distinct as the different wild-type kinases are from each other, in terms of inhibition profile. Considering mutants, then, the cancer-relevant druggable kinome is many thousand distinct targets, not 518 “plus a few”. Exposing this reality is a first step towards developing all the therapeutic tools necessary to ultimately defeat cancer. Here, we reveal vast and specific repurposing opportunities among the existing pharmacopeia for tumors that are driven by kinases not previously recognized as targets for certain drugs, and for tumors that have become chemotherapy-resistant through mutation of specific kinases. The most exciting and clinically relevant opportunities were confirmed in cell-based assays, results of which will also be presented. Citation Format: Christian M. Loch, Shuguang Liang, Jianghong Wu, Mia Eason, Alison Goupil, Anthony Acinapura, Jamie Purcell, Maia Dominguez. Screening the entire kinase-directed, FDA-approved pharmacopeia against the largest collection of wild-type and mutant kinases reveals many opportunities for drug repurposing and targeted therapy [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 938.

Abstract 4894: Multi-omic approach for drug repurposing in the poor prognosis CMS4 subtype of colon cancer

Abstract Introduction: Colon cancer (CC) is one of the deadliest malignancy in developing countries. Key drivers considered for the clinical management are RAS, BRAF alterations together with TNM staging and microsatellite status (MS). With the advancement of sequencing technologies and bioinformatic approaches, various molecular classifications of CC have been proposed. Guinney et al. proposed a transcriptomic-based molecular subtyping, the so-called four Consensus Molecular Subtypes (CMS). CMS4 patients have the worst prognosis. Biologically, CMS4, also known as mesenchymal, shows high expression of genes related to EMT, matrix remodelling, TGFb signaling, and inflammatory-related system and a peculiar enrichment in stromal cells. Thus, in the present study we are focusing on CMS4 subgroup in order to identify drugs to be repurposed in such a clinical setting through a multi-omic approach. Methods: Using TCGAbiolinks R package, we retrieved STAR Counts transcriptome profiling data, for COAD and READ. CMSclassifier was used to determine the CMS of the samples. We separated the coding genes from the long noncoding RNAs (lncRNAs) using annotation databases generated from Ensembl. We estimated the fraction of cell populations throughout the CIBERSORTx. Features selection approaches was performed both for coding genes and lncRNA. Data normalization has been carried out through variance stabilizing transformation for read count data. To identify CMS and potential biomarkers, we adopted mixOmics N-integration method. Our independent validation cohort consists of 100 patients, locally enrolled, upon local Ethical Committee approval. Features contributing to CMS4 subtype have been extracted and use to construct drug-gene interaction network. Results: To set-up the classification model the DIABLO approach was used, reaching a mean AUCROC of 0.9022. Contributing features related CMS4 subtypes included nine coding genes, six lncRNAs and, regarding CibersortX deconvolution, Macrophage M0 and M2.The drug-gene interaction network includes six subnetworks centered to ALOX5, KCNMA1, AQP9, TGFB3, THBS4 and DPYSL3. The role of TGFb pathway was confirmed, considering the contribution to classification. Interestingly, we found interactions with Non-Steroid Anti-Inflammatory Drugs (NSAIDs), such as Mesalazine. Moreover, THBSA gene, involved in cell-to-cell, cell-to-matrix and stromal response, could also be targeted. Conclusions: The results of our drug repositioning approach have been biologically corroborated considering, as mentioned above, the peculiar enrichment of CMS4 subtype for TGFb pathway and stromal cells. The chance to modulate the macrophagic activity in the tumor microenvironment through NSAIDs could be evaluated. Experimental validation on patient-derived organoids (PDOs), related to samples included to local validation cohort, is ongoing. Citation Format: Tommaso M. Marvulli, Debora Traversa, Roberta Di Fonte, Leonarda Maurmo, Amalia Azzariti, Letizia Porcelli, Claudio A. Coppola, Concetta Saponaro, Eliseo Mattioli, Francesco A. Zito, Rossella Fasano, Simona Serratì, Davide Quaresmini, Oronzo Brunetti, Stefania Tommasi, Raffaella Massafra, Simona De Summa. Multi-omic approach for drug repurposing in the poor prognosis CMS4 subtype of colon cancer [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 4894.

COVID-19 mortality paradox (United States vs Africa): Mass vaccination vs early treatment.

The coronavirus disease 2019 (COVID-19) mortality rate in 55 African countries is almost 4.5 times lower than in the coronavirus disease 2019 (COVID-19) despite Africa having over 4.2 times more people. This mortality paradox is also evident when comparing Nigeria, a heavily populated, poorly vaccinated and weakly mandated country to Israel, a small, highly vaccinated and strictly mandated country. Nigeria has almost 4 times lower COVID mortality than Israel. In this Field of Vision perspective, I explain how this paradox has evolved drawing upon my academic, clinical and social experience. Since April 2020, I've developed and been using the Egyptian immune-modulatory Kelleni's protocol to manage COVID-19 patients including pediatric, geriatric, pregnant, immune-compromised and other individuals suffering from multiple comorbidities. It's unfortunate that severe acute respiratory syndrome coronavirus 2 is still evolving accompanied by more deaths. However in Africa, we've been able to live without anxiety or mandates throughout the pandemic because we trust science and adopted early treatment using safe, and effective repurposed drugs that have saved the majority of COVID-19 patients. This article represents an African and Egyptian tale of honor.

Emerging technologies for drug repurposing: Harnessing the potential of text and graph embedding approaches

Drug repurposing is an approach to identifying new uses for existing drugs, where advanced computational methods, such as text and graph embedding techniques, are playing an ever-increasing role. This review provides a timely overview of these embedding methods for drug repurposing and discusses their integration with machine learning. Text embedding techniques, such as Word2Vec, FastText, BERT, and Doc2Vec, enable the analysis of biomedical literature and clinical data to discover potential drug-disease relationships. These methods convert textual data into numerical representations, allowing for similarity calculations and predictive modeling. Several successful applications of text embedding for drug repurposing are highlighted. In addition, graph embedding methods, such as Node2Vec and GraphSAGE, are being employed to convert complex biological knowledge graphs into vector representations. These representations facilitate various network analysis tasks, including predicting drug-target interactions and identifying hidden associations between drugs and diseases. Case studies in both technologies demonstrate their effectiveness in drug repurposing. The advantages and limitations of both text and graph embedding technologies, and their complementarity with traditional structure-based approaches have been discussed. Finally, text and graph embedding methods can be employed in conjunction with traditional approaches of computational methods, which can offer a promising path to identifying novel drug repurposing opportunities, particularly for rare diseases.

Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation.

The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling the identification of new therapeutic applications for existing drugs. In the present study, a trained deep-learning model was employed to screen a library of FDA-approved drugs to discover novel inhibitors targeting JAK2. To accomplish this, reference datasets containing active and decoy compounds specific to JAK2 were obtained from the DUD-E database. RDKit, a cheminformatic toolkit, was utilized to extract molecular features from the compounds. The DeepChem framework's GraphConvMol, based on graph convolutional network models, was applied to build a predictive model using the DUD-E datasets. Subsequently, the trained deep-learning model was used to predict the JAK2 inhibitory potential of FDA-approved drugs. Based on these predictions, ribociclib, topiroxostat, amodiaquine, and gefitinib were identified as potential JAK2 inhibitors. Notably, several known JAK2 inhibitors demonstrated high potential according to the prediction results, validating the reliability of our prediction model. To further validate these findings and confirm their JAK2 inhibitory activity, molecular docking experiments were conducted using tofacitinib-an FDA-approved drug for JAK2 inhibition. Experimental validation successfully confirmed our computational analysis results by demonstrating that these novel drugs exhibited comparable inhibitory activity against JAK2 compared to tofacitinib. In conclusion, our study highlights how deep learning models can significantly enhance virtual screening efforts in drug discovery by efficiently identifying potential candidates for specific targets such as JAK2. These newly discovered drugs hold promises as novel JAK2 inhibitors deserving further exploration and investigation.

Digital Technology Applications in the Management of Adverse Drug Reactions: Bibliometric Analysis.

Adverse drug reactions continue to be not only one of the most urgent problems in clinical medicine, but also a social problem. The aim of this study was a bibliometric analysis of the use of digital technologies to prevent adverse drug reactions and an overview of their main applications to improve the safety of pharmacotherapy. The search was conducted using the Web of Science database for the period 1991-2023. A positive trend in publications in the field of using digital technologies in the management of adverse drug reactions was revealed. A total of 72% of all relevant publications come from the following countries: the USA, China, England, India, and Germany. Among the organizations most active in the field of drug side effect management using digital technologies, American and Chinese universities dominate. Visualization of publication keywords using VOSviewer software 1.6.18 revealed four clusters: "preclinical studies", "clinical trials", "pharmacovigilance", and "reduction of adverse drug reactions in order to improve the patient's quality of life". Molecular design technologies, virtual models for toxicity modeling, data integration, and drug repurposing are among the key digital tools used in the preclinical research phase. Integrating the application of machine learning algorithms for data analysis, monitoring of electronic databases of spontaneous messages, electronic medical records, scientific databases, social networks, and analysis of digital device data into clinical trials and pharmacovigilance systems, can significantly improve the efficiency and safety of drug development, implementation, and monitoring processes. The result of combining all these technologies is a huge synergistic provision of up-to-date and valuable information to healthcare professionals, patients, and health authorities.

Screening effects of HCN channel blockers on sleep/wake behavior in zebrafish.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels generate electrical rhythmicity in various tissues although primarily heart, retina and brain. The HCN channel blocker compound, Ivabradine (Corlanor), is approved by the US Food and Drug Administration (FDA) as a medication to lower heart rate by blocking hyperpolarization activated inward current in the sinoatrial node. In addition, a growing body of evidence suggests a role for HCN channels in regulation of sleep/wake behavior. Zebrafish larvae are ideal model organisms for high throughput drug screening, drug repurposing and behavioral phenotyping studies. We leveraged this model system to investigate effects of three HCN channel blockers (Ivabradine, Zatebradine Hydrochloride and ZD7288) at multiple doses on sleep/wake behavior in wild type zebrafish. Results of interest included shorter latency to daytime sleep at 0.1 μM dose of Ivabradine (ANOVA, p: 0.02), moderate reduction in average activity at 30 μM dose of Zatebradine Hydrochloride (ANOVA, p: 0.024) in daytime, and increased nighttime sleep at 4.5 μM dose of ZD7288 (ANOVA, p: 0.036). Taken together, shorter latency to daytime sleep, decrease in daytime activity and increased nighttime sleep indicate that different HCN channel antagonists affected different parameters of sleep and activity.

Sildenafil as a Candidate Drug for Alzheimer's Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons.

Alzheimer's disease (AD) is a chronic neurodegenerative disease needing effective therapeutics urgently. Sildenafil, one of the approved phosphodiesterase-5 inhibitors, has been implicated as having potential effect in AD. To investigate the potential therapeutic benefit of sildenafil on AD. We performed real-world patient data analysis using the MarketScan® Medicare Supplemental and the Clinformatics® databases. We conducted propensity score-stratified analyses after adjusting confounding factors (i.e., sex, age, race, and comorbidities). We used both familial and sporadic AD patient induced pluripotent stem cells (iPSC) derived neurons to evaluate the sildenafil's mechanism-of-action. We showed that sildenafil usage is associated with reduced likelihood of AD across four new drug compactor cohorts, including bumetanide, furosemide, spironolactone, and nifedipine. For instance, sildenafil usage is associated with a 54% reduced incidence of AD in MarketScan® (hazard ratio [HR] = 0.46, 95% CI 0.32- 0.66) and a 30% reduced prevalence of AD in Clinformatics® (HR = 0.70, 95% CI 0.49- 1.00) compared to spironolactone. We found that sildenafil treatment reduced tau hyperphosphorylation (pTau181 and pTau205) in a dose-dependent manner in both familial and sporadic AD patient iPSC-derived neurons. RNA-sequencing data analysis of sildenafil-treated AD patient iPSC-derived neurons reveals that sildenafil specifically target AD related genes and pathobiological pathways, mechanistically supporting the beneficial effect of sildenafil in AD. These real-world patient data validation and mechanistic observations from patient iPSC-derived neurons further suggested that sildenafil is a potential repurposable drug for AD. Yet, randomized clinical trials are warranted to validate the causal treatment effects of sildenafil in AD.

Hematopoietic cell kinase as a nexus for drug repurposing: implications for cancer and HIV therapy

Hematopoietic cell kinase (HCK) has emerged as a potential target for therapeutic intervention in cancer and HIV infection because of its critical role in critical signaling pathways. Repurposing FDA-approved drugs offers an efficient strategy to identify new treatment options. Here, we address the need for novel therapies in cancer and HIV by investigating the potential of repurposed drugs against HCK. Our goal was to identify promising drug candidates with high binding affinities and specific interactions within the HCK binding pocket. We employed an integrated computational approach combining molecular docking and extensive molecular dynamics (MD) simulations. Initially, we analyzed the binding affinities and interaction patterns of a library of FDA-approved drugs sourced from DrugBank. After careful analysis, we focused on two compounds, Nilotinib and Radotinib, which exhibit exceptional binding affinities and specificity to the HCK binding pocket, including the active site. Additionally, we assessed the pharmacological properties of Nilotinib and Radotinib, making them attractive candidates for further drug development. Extensive all-atom MD simulations spanning 200 nanoseconds (ns) elucidated the conformational dynamics and stability of the HCK-Nilotinib and HCK-Radotinib complexes. These simulations demonstrate the robustness of these complexes over extended timescales. Our findings highlighted the potential of Nilotinib and Radotinib as promising candidates against HCK that offer valuable insights into their binding mechanisms. This computational approach provides a comprehensive understanding of drug interactions with HCK and sets the stage for future experimental validation and drug development endeavors. Communicated by Ramaswamy H. Sarma

Identifying novel aryl hydrocarbon receptor (AhR) modulators from clinically approved drugs: In silico screening and In vitro validation

The aryl hydrocarbon receptor (AhR) functions as a vital ligand-activated transcription factor, governing both physiological and pathophysiological processes. Notably, it responds to xenobiotics, leading to a diverse array of outcomes. In the context of drug repurposing, we present here a combined approach of utilizing structure-based virtual screening and molecular dynamics simulations. This approach aims to identify potential AhR modulators from Drugbank repository of clinically approved drugs. By focusing on the AhR PAS-B binding pocket, our screening protocol included binding affinities calculations, complex stability, and interactions within the binding site as a filtering method. Comprehensive evaluations of all DrugBank small molecule database revealed ten promising hits. This included flibanserin, butoconazole, luliconazole, naftifine, triclabendazole, rosiglitazone, empagliflozin, benperidol, nebivolol, and zucapsaicin. Each exhibiting diverse binding behaviors and remarkably very low binding free energy. Experimental studies further illuminated their modulation of AhR signaling, and showing that they are consistently reducing AhR activity, except for luliconazole, which intriguingly enhances the AhR activity. This work demonstrates the possibility of using computational modelling as a quick screening tool to predict new AhR modulators from extensive drug libraries. Importantly, these findings hold immense therapeutic potential for addressing AhR-associated disorders. Consequently, it offers compelling prospects for innovative interventions through drug repurposing.

Evaluating synergistic effects of metformin and simvastatin on ovarian cancer cells.

Ovarian Cancer (OC) stands as the most lethal gynecological malignancy, presenting an urgent clinical challenge in the quest to improve response rates. One approach to address this challenge is through drug repurposing, exemplified by the investigation of metabolic-modulating drugs such as Metformin (MTF) and Simvastatin (SIM). This study aims to explore the molecular mechanisms contributing to the potential synergistic anti-cancer effects between MTF and SIM on ovarian cancer cells. We assessed the effects of the combination on the proliferation and viability of two cell lines OVCAR-3 and SKOV-3. IC50 concentrations of MTF and SIM were determined using a proliferation assay, followed by subtoxic concentrations to explore the potential synergistic effects on the viability of both cell lines. Transcriptomic analysis was conducted on OVCAR-3 treated cells, and the findings were validated by assessing the expression levels of differentially expressed genes (DEGs) through real-time PCR in both cell lines SK-OV-3 and OVCAR-3. Cytotoxicity analysis guided the selection of treatment concentrations as such MTF 10 mM and SIM 5 μM. The combined treatment of MTF and SIM demonstrated a synergistic inhibition of proliferation and viability in both cell lines. In OVCAR-3, exclusive identification of 507 DEGs was seen in the combination arm. Upregulation of FOXO3, RhoA, and TNFα, along with downregulation of PIK3R1, SKP2, and ATP6V1D levels, was observed in OVCAR-3 treated cells. Real-time PCR validation confirmed the consistency of expression levels for the mentioned DEGs. Our data strongly supports the presence of synergy between MTF and SIM in OC cells. The combination's effect is associated with the dysregulation of genes in the key regulators AMPK and mTOR alongside other interconnected pathways.

Raloxifene-driven benzothiophene derivatives: Discovery, structural refinement, and biological evaluation as potent PPARγ modulators based on drug repurposing

By virtue of the drug repurposing strategy, the anti-osteoporosis drug raloxifene was identified as a novel PPARγ ligand through structure-based virtual high throughput screening (SB-VHTS) of FDA-approved drugs and TR-FRET competitive binding assay. Subsequent structural refinement of raloxifene led to the synthesis of a benzothiophene derivative, YGL-12. This compound exhibited potent PPARγ modulation with partial agonism, uniquely promoting adiponectin expression and inhibiting PPARγ Ser273 phosphorylation by CDK5 without inducing the expression of adipongenesis associated genes, including PPARγ, aP2, CD36, FASN and C/EBPα. This specific activity profile resulted in effective hypoglycemic properties, avoiding major TZD-related adverse effects like weight gain and hepatomegaly, which were demonstrated in db/db mice. Molecular docking studies showed that YGL-12 established additional hydrogen bonds with Ile281 and enhanced hydrogen-bond interaction with Ser289 as well as PPARγ Ser273 phosphorylation-related residues Ser342 and Glu343. These findings suggested YGL-12 as a promising T2DM therapeutic candidate, thereby providing a molecular framework for the development of novel PPARγ modulators with an enhanced therapeutic index.

Drug Repurposing of Metformin for the Treatment of Haloperidol-Related Behavior Disorders and Oxidative Stress: A Preliminary Study.

A particular attribute of the brain lies in the ability to learn, acquire information from the environment, and utilize the learned information. Previous research has noted that various factors (e.g., age, stress, anxiety, pathological issues), including antipsychotic medications, affect the brain and memory. The current study aimed to reveal the effects of chronic metformin treatment on the cognitive performance of rats and on commonly measured markers for oxidative stress. Wistar male rats (n = 40) were randomly divided into four groups: CTR (n = 10)-control group, METF (n = 10)-animals receiving metformin 500 mg/kg, HAL (n = 10)-animals receiving haloperidol 2 mg/kg, and HALMETF (n = 10)-animals receiving haloperidol 2 mg/kg and metformin 500 mg/kg. The medication was administered daily by oral gavage for 40 days. Memory and learning were assessed using the Morris Water Maze (MWM) test. At the end of the MWM, the rodents were decapitated under anesthesia, and the brain and blood samples were assayed by liquid chromatography for markers of oxidative stress (malondialdehyde, MDA, reduced/oxidized glutathione ratio, GSH/GSSG). The quantification of brain-derived neurotrophic factor (BDNF) was performed using the conventional sandwich ELISA technique. In the HALMETF group, metformin attenuated the negative effects of haloperidol. Brain and plasma MDA levels increased in the HAL group. Brain and plasma GSH/GSSG ratios and BDNF levels did not reveal any differences between groups. In conclusion, metformin treatment limits the deleterious cognitive effects of haloperidol. The effect on oxidative stress markers may also point toward an antioxidant-like effect of metformin, but this needs further tests for confirmation.

Lomitapide as a Potential Estrogen Receptor Inhibitor: A Computational Drug Repurposing Study

Objective: Estrogen receptor (ER) inhibitors have significant therapeutic potential for hormone-dependent cancers and related disorders. Tamoxifen, a well-known selective estrogen receptor modulator, has been widely used as adjuvant therapy for estrogen receptor-positive breast cancer. However, tamoxifen may exhibit a tendency to develop resistance with prolonged usage and particularly elevate the risk of uterine cancer. Therefore, there is a need for the discovery and development of new ER modulators or inhibitors. In this study, we identified potential estrogen receptor inhibitors through computational drug repositioning.
 
 Methods: A set of 2048 compounds, encompassing FDA-approved drugs and active metabolites, were subjected to molecular docking, molecular dynamics simulations, and free energy calculations to evaluate their interaction with estrogen receptor α (ERα).
 
 Results: Among the compounds evaluated, conivaptan, atogepant, and lomitapide exhibited the highest affinities for ERα. Lomitapide displayed a superior docking score (-12 kcal/mol) compared to the established ER inhibitor, tamoxifen (-10 kcal/mol). Further investigation using molecular dynamics simulations and free energy calculations disclosed lomitapide's heightened binding affinity of -380.727 kJ/mol, surpassing tamoxifen's binding affinity of -352.029 kJ/mol.
 
 Conclusion: This comprehensive computational exploration underscores lomitapide's potential as a compelling candidate with an envisaged stronger estrogen receptor affinity than the acknowledged standard, tamoxifen. To validate lomitapide's promise as a novel ER inhibitor, essential in vitro and in vivo studies are suggested. These investigations will provide essential insights into lomitapide's reposition in addressing the challenges tied to hormone-dependent cancers and associated maladies.

EKGDR: An End-to-End Knowledge Graph-Based Method for Computational Drug Repurposing.

The lengthy and expensive process of developing new drugs from scratch, coupled with a high failure rate, has prompted the emergence of drug repurposing/repositioning as a more efficient and cost-effective approach. This approach involves identifying new therapeutic applications for existing approved drugs, leveraging the extensive drug-related data already gathered. However, the diversity and heterogeneity of data, along with the limited availability of known drug-disease interactions, pose significant challenges to computational drug design. To address these challenges, this study introduces EKGDR, an end-to-end knowledge graph-based approach for computational drug repurposing. EKGDR utilizes the power of a drug knowledge graph, a comprehensive repository of drug-related information that encompasses known drug interactions and various categorization information, as well as structural molecular descriptors of drugs. EKGDR employs graph neural networks, a cutting-edge graph representation learning technique, to embed the drug knowledge graph (nodes and relations) in an end-to-end manner. By doing so, EKGDR can effectively learn the underlying causes (intents) behind drug-disease interactions and recursively aggregate and combine relational messages between nodes along different multihop neighborhood paths (relational paths). This process generates representations of disease and drug nodes, enabling EKGDR to predict the interaction probability for each drug-disease pair in an end-to-end manner. The obtained results demonstrate that EKGDR outperforms previous models in all three evaluation metrics: area under the receiver operating characteristic curve (AUROC = 0.9475), area under the precision-recall curve (AUPRC = 0.9490), and recall at the top-200 recommendations (Recall@200 = 0.8315). To further validate EKGDR's effectiveness, we evaluated the top-20 candidate drugs suggested for each of Alzheimer's and Parkinson's diseases.

Dominant dystrophic epidermolysis bullosa is associated with glycolytically active GATA3+ Th2 cells which may contribute to pruritus in lesional skin.

Dominant dystrophic epidermolysis bullosa (DDEB) is characterized by trauma-induced blisters and, in some individuals, intense pruritus. Precisely what causes itch in DDEB and optimal ways to reduce it have not been fully determined. To characterize DDEB skin transcriptomes to identify therapeutic targets to reduce pruritus in patients. We evaluated affected and unaffected skin biopsy samples from 6 DDEB subjects (all with the very itchy pruriginosa subtype), and 4 healthy individuals using bulk RNA-seq. Single-cell transcriptomes of affected (n=2) and unaffected (n=1) DDEB and healthy skin (n=2) were obtained. Dupilumab treatment was provided for three patients. The skin bulk transcriptome showed significant enrichment of Th1/2 and Th17 pathways in affected DDEB skin compared with non-lesional DDEB and healthy skin. Single-cell transcriptomics showed an association of glycolytically active GATA3+ Th2 cells in affected DDEB skin. Treatment with dupilumab in three people with DDEB led to significantly reduced VAS itch scores after 12 weeks (mean VAS=3.83) compared to pre-treatment (mean VAS=7.83). Bulk RNA-seq and qPCR showed that healthy skin and dupilumab-treated epidermolysis bullosa (EB) pruriginosa skin show very similar transcriptomic profiles, and reduced Th1/2 and Th17 pathway enrichment. Single-cell RNA-seq helps define an enhanced DDEB-associated Th2 profile and rationalizes drug repurposing of anti-Th2 drugs in treating DDEB pruritus.

Basal–epithelial subpopulations underlie and predict chemotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, characterized by extensive intratumoral heterogeneity, high metastasis, and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of these aggressive behaviors remains poorly understood. Using single-cell and spatial transcriptome analysis, here we discovered basal epithelial subpopulations located within the stroma that exhibit chemoresistance characteristics. The subpopulations are defined by distinct signature genes that show a frequent gain in copy number and exhibit an activated epithelial-to-mesenchymal transition program. A subset of these genes can accurately predict chemotherapy response and are associated with poor prognosis. Interestingly, among these genes, elevated ITGB1 participates in enhancing intercellular signaling while ACTN1 confers a survival advantage to foster chemoresistance. Furthermore, by subjecting the transcriptional signatures to drug repurposing analysis, we find that chemoresistant tumors may benefit from distinct inhibitors in treatment-naive versus post-NAC patients. These findings shed light on the mechanistic basis of chemoresistance while providing the best-in-class biomarker to predict chemotherapy response and alternate therapeutic avenues for improved management of TNBC patients resistant to chemotherapy.

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.