Drug Repurposing Research Articles

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer

BackgroundThe need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health.MethodsIn this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116).ResultsAccording to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29).ConclusionsCost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Drug Repurposing of ACT001 to Discover Novel Promising Sulfide Prodrugs with Improved Safety and Potent Activity for Neutrophil-Mediated Antifungal Immunotherapy.

Neutrophil-mediated immunotherapy is a promising strategy for treating Candida albicans infection due to its potential in dealing with drug-resistant events. Our previous study found that ACT001 exhibited good antifungal immunotherapeutic activity by inhibiting PD-L1 expression in neutrophils, but its strong cytotoxicity and high BBB permeability hindered its antifungal application. To address these deficiencies, a series of novel sulfide derivatives were designed and synthesized based on a slow-release prodrug strategy. Among these derivatives, compound 16 exhibited stronger inhibition of PD-L1 expression, less cytotoxicity to neutrophils, and lower BBB permeability than ACT001. Compound 16 also significantly enhanced neutrophil-mediated antifungal immunity in C. albicans infected mice, with acceptable pharmacokinetic properties and good oral safety. Moreover, pharmacological mechanism studies demonstrated that ACT001 and compound 16 reduced PD-L1 expression in neutrophils by directly targeting STAT3. Briefly, this study provided a novel prototype compound 16 which exhibited great potential in neutrophil-mediated antifungal immunotherapy.

In-silico Drug Repurposing of Abiraterone-based Compounds as 17α-Hydroxylase Inhibitors for Breast Cancer Treatment Using Drug-Drug Transcriptomic Similarity Analysis and Molecular Docking

This research explores abiraterone-based compounds’ in-silico drug repurposing potential as inhibitors of 17α-hydroxylase for breast cancer treatment, employing a multifaceted approach integrating molecular docking and drug-drug transcriptomic similarity analysis. Drug-drug transcriptomic similarity analysis revealed abiraterone to be the most transcriptomically similar compound, suggesting shared biological effects and repurposing opportunities. Molecular docking results identified abiraterone as a lead compound with a robust binding affinity and interacting amino acids within the active site of 17α-hydroxylase. Other compounds, including marbofloxacin, ataluren, zafirlukast, and montelukast, exhibited promising binding scores and diverse interactions, reinforcing their potential as potent inhibitors. Cell line-specific responses and connectivity patterns provided nuanced insights, guiding the selection of compounds. Overall, our findings underscore abiraterone-based compounds, especially abiraterone itself, as promising candidates for experimental validation, offering a significant stride in the pursuit of targeted and repurposed therapeutics for breast cancer treatment

Ferroptosis: Frenemy of Radiotherapy.

Recently, it was established that ferroptosis, a type of iron-dependent regulated cell death, plays a prominent role in radiotherapy-triggered cell death. Accordingly, ferroptosis inducers attracted a lot of interest as potential radio-synergizing drugs, ultimately enhancing radioresponses and patient outcomes. Nevertheless, the tumor microenvironment seems to have a major impact on ferroptosis induction. The influence of hypoxic conditions is an area of interest, as it remains the principal hurdle in the field of radiotherapy. In this review, we focus on the implications of hypoxic conditions on ferroptosis, contemplating the plausibility of using ferroptosis inducers as clinical radiosensitizers. Furthermore, we dive into the prospects of drug repurposing in the domain of ferroptosis inducers and radiosensitizers. Lastly, the potential adverse effects of ferroptosis inducers on normal tissue were discussed in detail. This review will provide an important framework for subsequent ferroptosis research, ascertaining the feasibility of ferroptosis inducers as clinical radiosensitizers.

Abstract 4713: Chlorpromazine affects glioblastoma bioenergetics by interfering with pyruvate kinase M2: A route for drug repurposing in glioblastoma

Abstract Glioblastoma, the most common and deadly brain tumor, remains a critical unmet medical need due to the limited effectiveness of current treatments. Drug repurposing has recently emerged as a promising strategy to improve glioblastoma outcomes. Antipsychotic drugs, with their established safety profile and potential to disrupt tumor-neuron interactions, have drawn particular attention. Among these, chlorpromazine, a well-tolerated medication included in the 2021 WHO Model List of Essential Medicines, holds promise due to its therapeutic effects in psychiatric disorders stemming from its non-specific interference with various CNS neurotransmitter receptors. Our recent studies have demonstrated chlorpromazine's ability to inhibit several molecular and cellular processes in glioblastoma cells, suggesting its potential as a novel treatment option for this challenging disease.To elucidate chlorpromazine's mechanism of action as a potential anticancer drug, we employed two proteomics approaches: Reverse-Phase Protein microArrays to evaluate its impact on signal transduction pathways and Activity-Based Protein Profiling followed by mass spectrometry to identify novel molecular targets.Our data revealed that chlorpromazine significantly modulates major signal transduction pathways and implicates pyruvate kinase (PK) M2 as a drug target. PKM2, a PK variant characteristic of many cancers, plays a crucial role in orchestrating metabolic alterations, exemplified by the Warburg effect – the high glucose consumption and lactate production by cancer cells even under oxygen-rich conditions. PKM2 functions as a tetramer in the glycolytic pathway, while its dimeric form acquires nuclear localization, protein kinase activity, and interacts with various transcription factors, contributing to its pro-tumorigenic activity.Consistent with its ability to target PKM2, chlorpromazine promoted PKM2 tetramerization in glioblastoma cells, leading to significant alterations in glioblastoma energy metabolism. Notably, RPE-1 non-cancer neuroepithelial cells showed a reduced response to the drug. Additionally, silencing PKM2 diminished the effects of chlorpromazine. 3D modeling revealed that chlorpromazine interacts with the PKM2 tetramer at the same site involved in binding other small-molecule activators that stabilize the PKM2 tetramer.These findings suggest that chlorpromazine counteracts the Warburg effect and, consequently, malignancy in glioblastoma cells, while sparing non-cancerous RPE-1 cells. This preclinical evidence supports the rationale behind our recently completed multicenter Phase II clinical trial investigating the role of chlorpromazine in glioblastoma treatment. The study is registered as EudraCT #2019-001988-75 and ClinicalTrials.gov Identifier #NCT0422444. Citation Format: Claudia Abbruzzese, Silvia Matteoni, Paola Matarrese, Michele Signore, Barbara Ascione, Elisabetta Iessi, Aymone Gurtner, Andrea Sacconi, Andrea Pace, Veronica Villani, Andrea Polo, Susan Costantini, Alfredo Budillon, Gennaro Ciliberto, Marco G. Paggi. Chlorpromazine affects glioblastoma bioenergetics by interfering with pyruvate kinase M2: A route for drug repurposing 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 4713.

Abstract 3100: Interactive analysis of drug testing data for functional precision oncology with Breeze 2.0

Abstract High throughput screening (HTS) is used to screen large numbers of small molecules in cell-based assays to predicting their potential therapeutic response and when applying approved drug libraries using HTS it becomes drug repurposing. The Drug Sensitivity and Resistance Testing (DSRT) platform established at FIMM has been extensively used in testing panels of approved and investigational drugs on cells, both conventional cell lines and primary cell models derived from cancer patient samples. The results help in deepen our understanding of drug efficacy in individual patients and across specific disease to guide future patient stratification. Breeze 2.0 is an open access, web-based platform for interactive analysis of drug screening data in real time. Breeze accepts raw data in a simple tabular format and generates detailed quality control metrics, plots, dose-response curve fits, drug response quantification in form of IC50/EC50/AUC and Drug Sensitivity Scores (DSS). Besides tabular results, Breeze generates interactive, informative, and intuitive visualizations, such as plate heatmaps, scatterplots, barplots, clustered heatmaps, circular plots, for further analysis. Based on manually-curated published datasets available through Breeze 2.0, users can instantly compare their drug response patterns with the existing DSRT profiles from patient samples, as well as cell lines. Importantly, Breeze 2.0 has integrated curated reference DSRT data tested on bone marrow samples from healthy control individuals, which plays a crucial role in understanding cancer-selective drug response by de-prioritizing generally toxic compounds. The end-users can select multiple healthy controls or relevant patient/cell line samples and choose the drugs of interest to visualize the overlaid dose responses, as well as response quantification metrics. Breeze 2.0 is built in R, PHP, MySQL and is freely accessible at https://breeze.fimm.fi/v2 Citation Format: Swapnil Potdar, Filipp Ianevski, Aleksandr Ianevski, Ziaurrehman Tanoli, Krister Wennerberg, Brinton Seashore-Ludlow, Olli Kallioniemi, Päivi Östling, Tero Aittokallio, Jani Saarela. Interactive analysis of drug testing data for functional precision oncology with Breeze 2.0 [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 3100.

Abstract 5978: Therapeutic targeting of MELK using a drug repurposing approach to combat TNBC cells

Abstract Background: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer linked to high chemo-resistant metastatic cases and limited therapeutic options due to the absence of conventional bioreceptors. Therefore, to treat TNBC, it is imperative to identify promising druggable biological targets. Experimental Procedures: An integrated bioinformatic analysis of microarray datasets was performed to identify significantly differentially expressed genes (DEGs) in TNBC. For selecting a potent therapeutic target, the function and significance of the upregulated DEGs in TNBC was investigated by an in silico study employing the UALCAN portal, String database, KM plotter, and Enrichr tools. This analysis included gene expression validation, insights into protein-protein interactions, survival analysis, and pathway mapping of the genes. To obtain potent drugs binding to the selected target MELK (maternal embryonic leucine zipper kinase), a drug repurposing approach was employed starting with virtual screening followed by molecular dynamic simulation (MDS) using GROMACS. Further, the anti-cancer activity of the top two drugs was validated by live-dead imaging, gene expression analyses, MTT-based cell viability and ROS detection assays. Results: Based on a stringent criterion for significant DEG identification (log2|FC|>1 & adjust P-value < 0.05), 25 overlapping upregulated genes were obtained from three selected datasets. Further, in silico analysis revealed that MELK is significantly upregulated across the four stages and subtypes of breast cancer, including TNBC. Higher MELK expression and its oncogenic interactions correlated with poor overall survival of TNBC patients. Evidence suggests that the existing inhibitor for MELK (OTSSP167) has shown poor specificity. Therefore, to block its activity, a library of 1293 FDA-approved drugs was screened against the kinase domain of MELK. MDS study and analysis using parameters like RMSD, RMSF and pair distance revealed that the top 10 drugs were strongly stabilized by H-bonds in the binding pocket of MELK, with minimal deviation and reduced fluctuations of MELK upon drug binding. Binding free energy calculation of drug-MELK interaction using the MM-PBSA method revealed that nine drugs possessed higher binding energy than the inhibitor. The top two drugs obtained from the study (originally kinase and reductase inhibitors respectively) were further validated for their in vitro anti-cancer efficacy in MDA-MB-231 and MDA-MB-468 cells. Treatment with these drugs exhibited (a) a dose-dependent decrease in proliferation, (b) significant cell death, (c) a marked increase in the cellular ROS levels, and (d) a decrease in the expression of MELK and its downstream targets in TNBC cell lines. Conclusion: The present study opens the avenue for using repurposed drugs as potential therapeutic molecules against MELK activity to combat TNBC progression. Citation Format: Arisha Arora, Shilpi Sarkar, Siddhartha S. Ghosh. Therapeutic targeting of MELK using a drug repurposing approach to combat TNBC cells [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 5978.

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.

Artificial intelligence-driven drug repositioning uncovers efavirenz as a modulator of α-synuclein propagation: Implications in Parkinson’s disease

Parkinson's disease (PD) is a complex neurodegenerative disorder with an unclear etiology. Despite significant research efforts, developing disease-modifying treatments for PD remains a major unmet medical need. Notably, drug repositioning is becoming an increasingly attractive direction in drug discovery, and computational approaches offer a relatively quick and resource-saving method for identifying testable hypotheses that promote drug repositioning. We used an artificial intelligence (AI)-based drug repositioning strategy to screen an extensive compound library and identify potential therapeutic agents for PD. Our AI-driven analysis revealed that efavirenz and nevirapine, approved for treating human immunodeficiency virus infection, had distinct profiles, suggesting their potential effects on PD pathophysiology. Among these, efavirenz attenuated α-synuclein (α-syn) propagation and associated neuroinflammation in the brain of preformed α-syn fibrils-injected A53T α-syn Tg mice and α-syn propagation and associated behavioral changes in the C. elegans BiFC model. Through in-depth molecular investigations, we found that efavirenz can modulate cholesterol metabolism and mitigate α-syn propagation, a key pathological feature implicated in PD progression by regulating CYP46A1. This study opens new avenues for further investigation into the mechanisms underlying PD pathology and the exploration of additional drug candidates using advanced computational methodologies.

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.