Efficacy and safety of ivermectin in patients with mild COVID-19 in Japan and Thailand

BACKGROUND

Ivermectin is an antiparasitic drug administered to hundreds of millions of people worldwide. Fundamental research suggests that ivermectin is effective against coronavirus disease 2019 (COVID-19); therefore, we investigated the efficacy and safety of ivermectin as a COVID-19 treatment option.

METHODS

This multi-regional (Japan and Thailand), multicenter, placebo-controlled, randomized, double-blind, parallel-group, Phase III study evaluated the efficacy and safety of ivermectin in patients with mild COVID-19 (IVERMILCO Study). The participants took a specified number of the investigational product (ivermectin or placebo) tablets of, adjusted to a dose of 0.3-0.4 mg/kg, orally on an empty stomach once daily for three days. The primary efficacy endpoint was the time at which clinical symptoms first showed an improving trend by 168 h after investigational product administration.

RESULTS

A total of 1030 eligible participants were assigned to receive the investigational product; 502 participants received ivermectin and 527 participants received a placebo. The primary efficacy endpoint was approximately 96 h (approximately four days) for both ivermectin and placebo groups, which did not show statistically significant difference (stratified log-rank test, p = 0.61). The incidence of adverse events and adverse drug reactions did not show statistically significant differences between the ivermectin and placebo groups (chi-square test, p = 0.97, p = 0.59).

CONCLUSIONS

The results show that ivermectin (0.3-0.4 mg/kg), as a treatment for patients with mild COVID-19, is ineffective; however, its safety has been confirmed for participants, including minor participants of 12 years or older (IVERMILCO Study ClinicalTrials.gov number, NCT05056883.).

Targeting cellular senescence as a therapeutic vulnerability in gastric cancer

AIMS

Cellular senescence (CS) represents an intracellular defense mechanism responding to stress signals and can be leveraged as a "vulnerability" in cancer treatment. This study aims to construct a CS atlas for gastric cancer (GC) and uncover potential therapeutics for GC patients.

MATERIALS AND METHODS

38 senescence-associated regulators with prognostic significance in GC were obtained from the CellAge database to construct Gastric cancer-specific Senescence Score (GSS). Using eXtreme Sum algorism, GSS-based drug repositioning was conducted to identify drugs that could antagonize GSS in CMap database. In vitro experiments were conducted to test the effect of combination of palbociclib and exisulind in eliminating GC cells.

KEY FINDINGS

Patients with high GSS exhibited CS-related features, such as CS markers upregulation, adverse clinical outcomes and hypomethylation status. scRNA-seq data showed malignant cells with high GSS exhibited enhanced senescence state and more immunosuppressive signals such as PVR-CD96 compared with malignant cells with low GSS. In addition, the GSS-High cancer associated fibroblasts might secrete cytokines and chemokines such as IL-6, CXCL1, CXCL12, and CCL2 to from an immunosuppressive microenvironment, and GSS could serve as an indicator for immunotherapy resistance. Exisulind exhibited the greatest potential to reverse GSS. In vitro experiments demonstrated that exisulind could induce apoptosis and suppress the proliferation of palbociclib-induced senescent GC cells.

SIGNIFICANCE

Overall, GSS offers a framework for better understanding of correlation between senescence and GC, which might provide new insights into the development of novel therapeutics in GC.

Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study

BACKGROUND

Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge.

RESULTS

In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes.

CONCLUSIONS

Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.

Quinine and chloroquine: Potential preclinical candidates for the treatment of tegumentary Leishmaniasis

Leishmaniasis is an endemic disease in more than 90 countries, constituting a relevant public health problem. Limited treatment options, increase in resistance, and therapeutic failure are important aspects for the discovery of new treatment options. Drug repurposing may accelerate the discovery of antiLeishmanial drugs. Recent tests indicating the in vitro potential of antimalarials Leishmania resulted in the design of this study. This study aimed at evaluating the susceptibility of Leishmania (L.) amazonensis to chloroquine (CQ) and quinine (QN), alone or in combination with amphotericin B (AFT) and pentamidine (PTN). In the in vitro tests, first, we evaluated the growth inhibition of 50 % of promastigotes (IC50) and cytotoxicity for HepG2 and THP-1 cells (CC50). The IC50 values of AFT and PNT were below 1 µM, while the IC50 values of CQ and QN ranged between 4 and 13 µM. Concerning cytotoxicity, CC50 values ranged between 7 and 30 µM for AFT and PNT, and between 22 and 157 µM for the antimalarials. We also calculated the Selectivity Index (SI), where AFT and PTN obtained the highest values, while the antimalarias obtained values between 5 and 12. Both antimalarials were additive (ƩFIC 1.05-1.8) in combination with AFT and PTN. For anti-amastigote activity, the drugs obtained the following ICA50 values: AFT (0.26 µM), PNT (2.09 µM), CQ (3.77 µM) and QN (24.5 µM). In the in vivo tests, we observed that the effective dose for the death of 50 % of parasites (ED50) of AFT and CQ were 0.63 mg/kg and 27.29 mg/kg, respectively. When combining CQ with AFT, a decrease in parasitemia was observed, being statistically equal to the naive group. For cytokine quantification, it was observed that CQ, despite presenting anti-inflammatory activity was effective at increasing the production of IFN-γ. Overall, our data indicate that chloroquine will probably be a candidate for repurposing and use in drug combination therapy.

Computational drug repurposing for primary hyperparathyroidism

In hyperparathyroidism (hyperPTH), excessive amounts of PTH are secreted, interfering with calcium regulation in the body. Several drugs can control the disease's side effects, but none of them is an alternative treatment to surgery. Therefore, new drug candidates are necessary. In this study, three computationally repositioned drugs, DG 041, IMD 0354, and cucurbitacin I, are evaluated in an in vitro model of hyperPTH. First, we integrated publicly available transcriptomics datasets to propose drug candidates. Using 3D spheroids derived from a single primary hyperPTH patient, we assessed their in vitro efficacy. None of the proposed drugs affected the viability of healthy cell control (HEK293) or overactive parathyroid cells at the level of toxicity. This behavior was attributed to the non-cancerous nature of the parathyroid cells, establishing the hyperPTH disease model. Cucurbitacin I and IMD 0354 exhibited a slight inverse relationship between increased drug concentrations and cell viability, whereas DG 041 increased viability. Based on these results, further studies are needed on the mechanism of action of the repurposed drugs, including determining the effects of these drugs on cellular PTH synthesis and secretion and on the metabolic pathways that regulate PTH secretion.

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.

Discovering therapeutic possibilities for polycystic ovary syndrome by targeting XIST and its associated ceRNA network through the analysis of transcriptome data

Long non-coding RNA (lncRNA) regulates many physiological processes by acting as competitive endogenous RNA (ceRNA). The dysregulation of lncRNA X-inactive specific transcript (XIST) has been shown in various human disorders. However, its role in the pathogenesis of polycystic ovary syndrome (PCOS) is yet to be explored. This study aimed to explore the underlying mechanism of XIST in the pathogenesis of PCOS, specifically through dataset functional analysis. GEO PCOS datasets including RNA-seq, microarray, and miRNA-seq in granulosa cells (GCs) and blood, were examined and comprehensively analyzed. Enrichment analysis, ROC curve constructions, lncRNA-miRNA-mRNA interaction network analyses, and qRT-PCR validation were performed followed by a series of drug signature screenings. Our results revealed significant dysregulation in the expression of 1131 mRNAs, 30 miRNAs, and XIST in GCs of PCOS patients compared to healthy individuals. Of the120 XIST-correlated upregulated genes, 25 were enriched in inflammation-related pathways. Additionally, 5 miRNAs were identified as negative regulators of XIST-correlated genes. Accordingly, a ceRNA network containing XIST-miRNAs-mRNAs interactions was constructed. Furthermore, 6 genes, including AQP9, ETS2, PLAU, PLEK, SOCS3, and TNFRSF1B served as both GCs and blood-based biomarkers. By analyzing the number of interactions among XIST, miRNAs, and mRNAs, we pinpointed ETS2 as the pivotal gene within the ceRNA network. Our findings reveal a novel XIST- hsa-miR-146a-5p, hsa-miR-144-3p, and hsa-miR-1271-5p-ETS2 axis that comprehensively elucidates the XIST-associated mechanism underlying PCOS onset. qRT-PCR analysis further confirmed the, overexpression of both XIST and ETS2 . Furthermore, our results demonstrated that XIST and ETS2 were correlated with some assisted reproductive technologies outcomes. Finally, we identified two novel compounds including, methotrexate/folate and threonine using drug-gene interaction databases for PCOS management. These findings provide novel insights into the molecular etiology, diagnosis, and potential therapeutic interventions for PCOS.

Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity

Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug-disease heterogeneous networks to extract drug-disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The method utilizes a deep sparse autoencoder and integrates drug-disease similarities. First, the researchers constructed a drug-disease feature network by incorporating information from drug chemical structure, disease semantic data, and existing known drug-disease associations. Then, we learned the low-dimensional representation of the feature network using a deep sparse autoencoder. Finally, we utilized a deep neural network to make predictions on new drug-disease associations based on the feature representation. The experimental results show that our proposed method has achieved optimal results on all four benchmark datasets, especially on the CTD dataset where AUC and AUPR reached 0.9619 and 0.9676, respectively, outperforming other baseline methods. In the case study, the researchers predicted the top ten antiviral drugs for COVID-19. Remarkably, six out of these predictions were subsequently validated by other literature sources.

The exoprotein Gbp of Fusobacterium nucleatum promotes THP-1 cell lipid deposition by binding to CypA and activating PI3K-AKT/MAPK/NF-κB pathways

INTRODUCTION

Growing evidence has shown the correlation between periodontitis and atherosclerosis, while our knowledge on the pathogenesis of periodontitis-promoting atherosclerosis is far from sufficient.

OBJECTIVES

Illuminate the pathogenic effects of Fusobacterium nucleatum (F. nucleatum) on intracellular lipid deposition in THP-1-derived macrophages and elucidate the underlying pathogenic mechanism of how F. nucleatum promoting atherosclerosis.

METHODS AND RESULTS

F. nucleatum was frequently detected in different kinds of atherosclerotic plaques and its abundance was positively correlated with the proportion of macrophages. In vitro assays showed F. nucleatum could adhere to and invade THP-1 cells, and survive continuously in macrophages for 24 h. F. nucleatum stimulation alone could significantly promote cellular inflammation, lipid uptake and inhibit lipid outflow. The dynamic gene expression of THP-1 cells demonstrated that F. nucleatum could time-serially induce the over-expression of multiple inflammatory related genes and activate NF-κB, MAPK and PI3K-AKT signaling pathways. The exoprotein of F. nucleatum, D-galactose-binding protein (Gbp), acted as one of the main pathogenic proteins to interact with the Cyclophilin A (CypA) of THP-1 cells and induced the activation of the NF- κB, MAPK and PI3K-AKT signaling pathways. Furthermore, use of six candidate drugs targeting to the key proteins in NF- κB, MAPK and PI3K-AKT pathways could dramatically decrease F. nucleatum induced inflammation and lipid deposition in THP-1 cells.

CONCLUSIONS

This study suggests that the periodontal pathogen F. nucleatum can activate macrophage PI3K-AKT/MAPK/NF-κB signal pathways, promotes inflammation, enhances cholesterol uptake, reduces lipid excretion, and promotes lipid deposition, which may be one of its main strategies promoting the development of atherosclerosis.

Integration of National Health Insurance claims data and animal models reveals fexofenadine as a promising repurposed drug for Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a common and costly progressive neurodegenerative disease of unclear etiology. A disease-modifying approach that can directly stop or slow its progression remains a major unmet need in the treatment of PD. A clinical pharmacology-based drug repositioning strategy is a useful approach for identifying new drugs for PD.

METHODS

We analyzed claims data obtained from the National Health Insurance Service (NHIS), which covers a significant portion of the South Korean population, to investigate the association between antihistamines, a class of drugs commonly used to treat allergic symptoms by blocking H1 receptor, and PD in a real-world setting. Additionally, we validated this model using various animal models of PD such as the 6-hydroxydopmaine (6-OHDA), α-synuclein preformed fibrils (PFF) injection, and Caenorhabditis elegans (C. elegans) models. Finally, whole transcriptome data and Ingenuity Pathway Analysis (IPA) were used to elucidate drug mechanism pathways.

RESULTS

We identified fexofenadine as the most promising candidate using National Health Insurance claims data in the real world. In several animal models, including the 6-OHDA, PFF injection, and C. elegans models, fexofenadine ameliorated PD-related pathologies. RNA-seq analysis and the subsequent experiments suggested that fexofenadine is effective in PD via inhibition of peripheral immune cell infiltration into the brain.

CONCLUSION

Fexofenadine shows promise for the treatment of PD, identified through clinical data and validated in diverse animal models. This combined clinical and preclinical approach offers valuable insights for developing novel PD therapeutics.

Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction

BACKGROUND

Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data.

RESULTS

In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation.

CONCLUSIONS

Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.

Pramipexole protects against diabetic neuropathy: Effect on oxidative stress, TLR4/IRAK-1/TRAF-6/NF-κB and downstream inflammatory mediators

BACKGROUND

Diabetic neuropathy (DN) is a serious microvascular complication and a major cause of morbidity and mortality in diabetes mellitus. It is characterized by neurodegeneration of terminal sensory nerve fibers with subsequent pain, loss of sensation, and paresthesia, thus compromising the quality of life of diabetic patients. It is considered the leading cause of non-traumatic amputations worldwide, reflecting the insufficiency of current therapies. Pramipexole (PPX) is a dopamine receptor agonist used for the treatment of Parkinson's disease. The current study aims to investigate the potential neuroprotective effect of PPX in an experimental model of DN.

METHODS

Sprague Dawley rats were randomly assigned into five groups: normal control, Normal + PPX (1 mg/kg) group, STZ control, STZ + PPX (0.25 and 1 mg/kg/day for eight weeks). The neuroprotective effect of PPX in rats was evaluated in terms of sciatic nerve histological alterations, oxidative stress, and protein expression of TLR4/MyD88/IRAK-1/TRAF-6/NF-κB axis and downstream inflammatory mediators.

RESULTS

PPX administration ameliorated histopathological signs of neuronal inflammation and apoptosis. Additionally, PPX attenuated STZ-induced sciatic nerve oxidative stress and downregulated neural tissue expression of TLR4, MyD88, IRAK-1, TRAF-6, NF-κB and downstream mediators (TNF-α, IL-1β and ICAM-1).

CONCLUSION

Collectively, the current study sheds light on PPX as a potential protective medication to alleviate neuropathy progression in diabetic patients. PPX neuroprotective effect can be attributed to modulating TLR4/ MyD88/IRAK-1/TRAF-6/ NF-κB axis signaling in nerve tissues with subsequent attenuation of oxidative stress and inflammation.

Repositioning FDA-Approved Drug Against Chagas Disease and Cutaneous Leishmaniosis by Structure-Based Virtual Screening

BACKGROUND

Chagas disease and cutaneous leishmaniasis, two parasitic diseases caused by Trypanosoma cruzi (T. cruzi) and Leishmania mexicana (L. mexicana), respectively, have a major global impact. Current pharmacological treatments for these diseases are limited and can cause severe side effects; thus, there is a need for new antiprotozoal drugs.

METHODS

Using molecular docking, this work describes a structure-based virtual screening of an FDA-approved drug library against Trypanosoma cruzi and Leishmania mexicana glycolytic enzyme triosephosphate isomerase (TIM), which is highly conserved in these parasites. The selected compounds with potential dual inhibitory activity were tested in vitro to confirm their biological activity.

RESULTS

The study showed that five compounds: nilotinib, chlorhexidine, protriptyline, cyproheptadine, and montelukast, were more active against T. cruzi, than the reference drugs, nifurtimox and benznidazole while chlorhexidine and protriptyline were the most active against L. mexicana.

CONCLUSIONS

The analysis of these compounds and their structural characteristics may provide the basis for the development of new antiprotozoal agents.

The interference between SARS-COV-2 and Alzheimer's disease: Potential immunological and neurobiological crosstalk from a kinase perspective reveals a delayed pandemic

Coronavirus disease 2019 (COVID-19) has infected over 700 million people, with up to 30% developing neurological manifestations, including dementias. However, there is a lack of understanding of common molecular brain markers causing Alzheimer's disease (AD). COVID-19 has etiological cofactors with AD, making patients with AD a vulnerable population at high risk of experiencing more severe symptoms and worse consequences. Both AD and COVID-19 have upregulated several shared kinases, leading to the repositioning of kinase inhibitors (KIs) for the treatment of both diseases. This review provides an overview of the interactions between the immune system and the nervous system in relation to receptor tyrosine kinases, including epidermal growth factor receptors, vascular growth factor receptors, and non-receptor tyrosine kinases such as Bruton tyrosine kinase, spleen tyrosine kinase, c-ABL, and JAK/STAT. We will discuss the promising results of kinase inhibitors in pre-clinical and clinical studies for both COVID-19 and Alzheimer's disease (AD), as well as the challenges in repositioning KIs for these diseases. Understanding the shared kinases between AD and COVID-19 could help in developing therapeutic approaches for both.

Drug Repositioning for Ovarian Cancer Treatment: An Update

Ovarian cancer (OC) is one of the most prevalent malignancies in female reproductive organs, and its 5-year survival is below 45%. Despite the advances in surgical and chemotherapeutic options, OC treatment is still a challenge, and new anticancer agents are urgently needed. Drug repositioning has gained significant attention in drug discovery, representing a smart way to identify new clinical applications for drugs whose human safety and pharmacokinetics have already been established, with great time and cost savings in pharmaceutical development endeavors. This review offers an update on the most promising drugs repurposable for OC treatment and/or prevention.

PDATC-NCPMKL: Predicting drug's Anatomical Therapeutic Chemical (ATC) codes based on network consistency projection and multiple kernel learning

The development and discovery of new drugs is time-consuming and needs lots of human and material resources. Therefore, discovery of novel effects of existing drugs is an important alternative way, which can accelerate the process of designing "new" drugs. The anatomical Therapeutic Chemical (ATC) classification system recommended by World Health Organization (WHO) is a basic research area in this regard. A novel ATC code of an existing drug suggests its novel effects. Some computational models have been proposed, which can predict the drug-ATC code associations. However, their performance is not very high. There still exist spaces for improvement. In this study, a new recommendation system (named PDATC-NCPMKL), which incorporated network consistency projection and multi-kernel learning, was designed to identify drug-ATC code associations. For drugs or ATC codes, several kernels were constructed, which were fused by a multiple kernel learning method and an additional kernel integration scheme. To enhance the performance, the drug-ATC code association adjacency matrix was reformulated by a variant of weighted K nearest known neighbors (WKNKN). The reformulated adjacency matrix, drug and ATC code kernels were fed into network consistency projection to generate the association score matrix. The proposed recommendation system was tested on the ATC codes at the second, third and fourth levels in drug ATC classification system using ten-fold cross-validation. The results indicated that all AUROC and AUPR values were close to or exceeded 0.96. Such performance was higher than some existing computational models. Some additional tests were conducted to prove the utility of adjacency matrix reformulation and to analyze the importance of drug and ATC code kernels.

Identification of new potential candidates to inhibit EGF via machine learning algorithm

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

Protein-centric omics analysis reveals circulating complements linked to non-viral liver diseases as potential therapeutic targets

BACKGROUND/AIMS

To evaluate the causal correlation between complement components and non-viral liver diseases and their potential use as druggable targets.

METHODS

We conducted Mendelian randomization (MR) to assess the causal role of circulating complements in the risk of non-viral liver diseases. A complement-centric protein interaction network was constructed to explore biological functions and identify potential therapeutic options.

RESULTS

In the MR analysis, genetically predicted levels of complement C1q C chain (C1QC) were positively associated with the risk of autoimmune hepatitis (odds ratio 1.125, 95% confidence interval 1.018-1.244), while complement factor H-related protein 5 (CFHR5) was positively associated with the risk of primary sclerosing cholangitis (PSC;1.193, 1.048- 1.357). On the other hand, CFHR1 (0.621, 0.497-0.776) and CFHR2 (0.824, 0.703-0.965) were inversely associated with the risk of alcohol-related cirrhosis. There were also significant inverse associations between C8 gamma chain (C8G) and PSC (0.832, 0.707-0.979), as well as the risk of metabolic dysfunction-associated steatotic liver disease (1.167, 1.036-1.314). Additionally, C1S (0.111, 0.018-0.672), C7 (1.631, 1.190-2.236), and CFHR2 (1.279, 1.059-1.546) were significantly associated with the risk of hepatocellular carcinoma. Proteins from the complement regulatory networks and various liver diseaserelated proteins share common biological processes. Furthermore, potential therapeutic drugs for various liver diseases were identified through drug repurposing based on the complement regulatory network.

CONCLUSION

Our study suggests that certain complement components, including C1S, C1QC, CFHR1, CFHR2, CFHR5, C7, and C8G, might play a role in non-viral liver diseases and could be potential targets for drug development.

Integrated genomic network analysis revealed potential of a druggable target for hemorrhoid treatment

Hemorrhoids are a prevalent medical condition that necessitates effective treatment options. The current options for treatment consist of oral medications, topical applications, or surgery, yet a scarcity of highly effective drugs still exists. Genetic markers provide promising avenues for investigating the treatment of hemorrhoids, as they may reveal intricate biological mechanisms and targeted drug therapies, ultimately enhancing more precise treatment tailored to the patient. This study aims to identify new drug candidates for treating hemorrhoids through a meticulous bioinformatics approach and integrated with genomic network analysis. After extracting 21 druggable target genes using DrugBank from 293 genes connected to hemorrhoids, 87 possible drugs were selected. Three of these drugs (ketamine, methylene blue, and fulvestrant) hold potential in addressing issues associated with hemorrhoids and have been supported by clinical or preclinical studies. Eighty-four compounds present new therapeutic possibilities for managing hemorrhoids. We highlight that our findings indicate that NOX1 and NOS3 genes are promising biomarkers, with NOS3 gaining significance owing to its robust systemic functional annotations. Sapropterin, an existing drug, is closely associated with NOS3, providing a clear target for biomarker-driven interventions. This study illustrates the potential of combining genomic network analysis with bioinformatics to repurpose drugs for treating hemorrhoids. Subsequent research will explore the mechanisms for utilizing NOS3 targeting in the treatment of hemorrhoids.

Identifying actionable druggable targets for breast cancer: Mendelian randomization and population-based analyses

BACKGROUND

Drug repurposing provides a cost-effective approach to address the need for breast cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR) and then validate the candidate drugs using population-based analyses.

METHODS

We identified genetic instruments for 1406 actionable targets of approved non-oncological drugs based on gene expression, DNA methylation, and protein expression quantitative trait loci (eQTL, mQTL, and pQTL, respectively). Genome-wide association study (GWAS) summary statistics were obtained from the Breast Cancer Association Consortium (122,977 cases, 105,974 controls). We further conducted a nested case-control study using data retrieved from Swedish registers to validate the candidate drugs that were identified from MR analyses.

FINDINGS

We identified six significant MR associations with gene expression levels (TUBB, MDM2, CSK, ULK3, MC1R and KCNN4) and two significant associations with gene methylation levels across 21 CpG islands (RPS23 and MAPT). Results from the nested case-control study showed that the use of raloxifene (targeting MAPT) was associated with 35% reduced breast cancer risk (odds ratio, OR, 0.65; 95% confidence interval, CI, 0.51-0.83). However, usage of estradiol, tolterodine, and nitrofurantoin (also targeting MAPT) was associated with increased breast cancer risk, with adjusted ORs and 95% CI of 1.10 (1.07-1.13), 1.16 (1.09-1.24), and 1.09 (1.05-1.13), respectively. The effect of raloxifene and nitrofurantoin lost significance in further validation analyses using active-comparator and new-user design.

INTERPRETATION

This large-scale MR analysis, combined with population-based validation, identified eight druggable target genes for breast cancer and suggested that raloxifene is an effective chemoprevention against breast cancer.

FUNDING

Swedish Research Council, Cancerfonden, Crafoordska Stiftelsen, Allmänna Sjukhusets i Malmö Stiftelsen för bekämpande av cancer, 111 Project and MAS cancer.

Metronomic chemotherapy in ovarian cancer

Translational research and the development of targeted therapies have transformed the therapeutic landscape in epithelial ovarian cancer over the last decade. However, recurrent ovarian cancer continues to pose formidable challenges to therapeutic interventions, necessitating innovative strategies to optimize treatment outcomes. Current research focuses on the development of pharmaceuticals that target potential resistance pathways to DNA repair pathways. However, the cost and toxicity of some of these therapies are prohibitive and majority of patients lack access to clinical trials. Metronomic chemotherapy, characterized by the continuous administration of low doses of chemotherapeutic agents without long treatment breaks, has emerged as a promising approach with potential implications beyond recurrent setting. It acts primarily by inhibition of angiogenesis and activation of host immune system. We here review the mechanism of action of metronomic chemotherapy, as well as its current role, limitations, and avenues for further research in the management of epithelial ovarian cancer.

Clofazimine: A journey of a drug

Among different strategies to develop novel therapies, drug repositioning (aka repurposing) aims at identifying new uses of an already approved or investigational drug. This approach has the advantages of availability of the extensive pre-existing knowledge of the drug's safety, pharmacology and toxicology, manufacturing and formulation. It provides advantages to the risk-versus-rewards trade-off as compared to the costly and time-consuming de novo drug discovery process. Clofazimine, a red-colored synthetic derivative of riminophenazines initially isolated from lichens, was first synthesized in the 1950 s, and passed through several phases of repositioning in its history as a drug. Being initially developed as an anti-tuberculosis treatment, it was repurposed for the treatment of leprosy, prior to re-repositioning for the treatment of multidrug-resistant tuberculosis and other infections. Since 1990 s, reports on the anticancer properties of clofazimine, both in vitro and in vivo, started to appear. Among the diverse mechanisms of action proposed, the activity of clofazimine as a specific inhibitor of the oncogenic Wnt signaling pathway has recently emerged as the promising targeting mechanism of the drug against breast, colon, liver, and other forms of cancer. Seventy years after the initial discovery, clofazimine's journey as a drug finding new applications continues, serving as a colorful illustration of drug repurposing in modern pharmacology.

Antifungal activity of selective serotonin reuptake inhibitors against Cryptococcus spp. and their possible mechanism of action

Fungal infections caused by Cryptococcus spp. pose a threat to health, especially in immunocompromised individuals. The available arsenal of drugs against cryptococcosis is limited, due to their toxicity and/or lack of accessibility in low-income countries, requiring more therapeutic alternatives. Selective serotonin reuptake inhibitors (SSRIs), through drug repositioning, are a promising alternative to broaden the range of new antifungals against Cryptococcus spp. This study evaluates the antifungal activity of three SSRIs, sertraline, paroxetine, and fluoxetine, against Cryptococcus spp. strains, as well as assesses their possible mechanism of action. Seven strains of Cryptococcus spp. were used. Sensitivity to SSRIs, fluconazole, and itraconazole was evaluated using the broth microdilution assay. The interactions resulting from combinations of SSRIs and azoles were investigated using the checkerboard assay. The possible action mechanism of SSRIs against Cryptococcus spp. was evaluated through flow cytometry assays. The SSRIs exhibited in vitro antifungal activity against Cryptococcus spp. strains, with minimum inhibitory concentrations ranging from 2 to 32 μg/mL, and had synergistic and additive interactions with azoles. The mechanism of action of SSRIs against Cryptococcus spp. involved damage to the mitochondrial membrane and increasing the production of reactive oxygen species, resulting in loss of cellular viability and apoptotic cell death. Fluoxetine also was able to cause significant damage to yeast DNA. These findings demonstrate the in vitro antifungal potential of SSRIs against Cryptococcus spp. strains.

Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data

Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the DISNET biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version's results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field.

Repurposing existing drugs for monkeypox: applications of virtual screening methods

BACKGROUND

Monkeypox is endemic to African region and has become of Global concern recently due to its outbreaks in non-endemic countries. Although, the disease was first recorded in 1970, no monkeypox specific drug or vaccine exists as of now.

METHODS

We applied drug repositioning method, testing effectiveness of currently approved drugs against emerging disease, as one of the most affordable approaches for discovering novel treatment measures. Techniques such as virtual ligand-based and structure-based screening were applied to identify potential drug candidates against monkeypox.

RESULTS

We narrowed down our results to 6 antiviral and 20 anti-tumor drugs that exhibit theoretically higher potency than tecovirimat, the currently approved drug for monkeypox disease.

CONCLUSIONS

Our results indicated that selected drug compounds displayed strong binding affinity for p37 receptor of monkeypox virus and therefore can potentially be used in future studies to confirm their effectiveness against the disease.

Application of Drug Repurposing Approach for Therapeutic Intervention of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD), represented by Crohn's disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disorder of the gastrointestinal tract (GIT) characterized by chronic relapsing intestinal inflammation, abdominal pain, cramping, loss of appetite, fatigue, diarrhoea, and weight loss. Although the etiology of IBD remains unclear, it is believed to be an interaction between genes, and environmental factors, such as an imbalance of the intestinal microbiota, changing food habits, an ultra-non-hygiene environment, and an inappropriate immune system. The development of novel effective therapies is stymied by a lack of understanding of the aetiology of IBD. The current therapy involves the use of aminosalicylates, immunosuppressants, and corticosteroids that can effectively manage symptoms, induce and sustain remission, prevent complications, modify the course of the disease, provide diverse treatment options, showcase advancements in biologic therapies, and enhance the overall quality of life. However, the efficacy of current therapy is overshadowed by a plethora of adverse effects, such as loss of weight, mood swings, skin issues, loss of bone density, higher vulnerability to infections, and elevated blood pressure. Biologicals, like anti-tumour necrosis factor agents, can stimulate an autoimmune response in certain individuals that may diminish the effectiveness of the medication over time, necessitating a switch to alternative treatments. The response of IBD patients to current drug therapy is quite varied, which can lead to disease flares that underlines the urgent need to explore alternative treatment option to address the unmet need of developing new treatment strategies for IBD with high efficacy and fewer adverse effects. Drug repurposing is a novel strategy where existing drugs that have already been validated safe in patients for the management of certain diseases are redeployed to treat other, unindicated diseases. The present narrative review focuses on potential drug candidates that could be repurposed for the management of IBD using on-target and off-target strategies. It covers their preclinical, clinical assessment, mechanism of action, and safety profiles, and forecasts their appropriateness in the management of IBD. The review presents useful insights into the most promising candidates for repurposing, like anti-inflammatory and anti-apoptotic troxerutin, which has been found to improve the DSS-induced colitis in rats, an antiosteoarthritic drug diacetylrhein that has been found to have remarkable ameliorating effects on DSS-induced colitis via anti-oxidant and anti-inflammatory properties and by influencing both apoptosis and pyroptosis. Topiramate, an antiepileptic and anticonvulsant drug, has remarkably decreased overall pathophysiological and histopathological events in the experimental model of IBD in rodents by its cytokine inhibitory action.

DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing

BACKGROUND

Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.

RESULTS

This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%.

CONCLUSION

We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .

Three candidate anticancer drugs were repositioned by integrative analysis of the transcriptomes of species with different regenerative abilities after injury

Regeneration is a homeostatic process that involves the restoration of cells and body parts. Most of the molecular mechanisms and signalling pathways involved in wound healing, such as proliferation, have also been associated with cancer cell growth, suggesting that cancer is an over/unhealed wound. In this study, we examined differentially expressed genes in spinal cord samples from regenerative organisms (axolotl and zebrafish) and nonregenerative organisms (mouse and rat) compared to intact control spinal cord samples using publicly available transcriptomics data and bioinformatics analyses. Based on these gene signatures, we investigated 3 small compounds, namely cucurbitacin I, BMS-754807, and PHA-793887 as potential candidates for the treatment of cancer. The predicted target genes of the repositioned compounds were mainly enriched with the greatest number of genes in cancer pathways. The molecular docking results on the binding affinity between the repositioned compounds and their target genes are also reported. The repositioned 3 small compounds showed anticancer effect both in 2D and 3D cell cultures using the prostate cancer cell line as a model. We propose cucurbitacin I, BMS-754807, and PHA-793887 as potential anticancer drug candidates. Future studies on the mechanisms associated with the revealed gene signatures and anticancer effects of these three small compunds would allow scientists to develop therapeutic approaches to combat cancer. This research contributes to the evaluation of mechanisms and gene signatures that either limit or cause cancer, and to the development of new cancer therapies by establishing a link between regeneration and carcinogenesis.

Analysis of differential gene immune infiltration and clinical characteristics of skin cutaneous melanoma based on systems biology and drug repositioning methods to identify drug candidates for skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) has a low early detection rate and a high mortality rate. There are many problems such as side effects and drug resistance in existing therapeutic drugs. Current studies have confirmed that SKCM pathogenesis-related genes promote the invasion and metastasis of cutaneous melanoma, but their roles in the tumor microenvironment (TME) remain unclear. Network pharmacology provides new opportunities for drug repurposing and repositioning, and is a fast, safe, and inexpensive drug discovery method to find new drugs for the treatment of SKCM. In this study, based on 3 databases (KEGG, OMIM, and Genotype) to obtain SKCM-related genes, and TCGA SKCM dataset, SKCM differential genes in GSE3189 and GSE46517 were intersected to identify SKCM pathogenesis-related differential genes, and the differential genes were immune infiltration and analysis, For survival analysis, a prognostic nomogram risk model was constructed based on the results of multivariate Cox regression analysis for risk stratification and prognosis prediction, then focused on the differential expression of ZC3H12A and its effect on TME. Finally, the protein interaction network method was used to quantify the similarity between 684 drug targets and skin melanoma, and to screen out drugs similar to skin melanoma. Based on 3 databases of KEGG, OMIM, and Genotype, 294 SKCM-related genes and 18 SKCM pathogenesis-related differential genes were obtained, and 18 SKCM pathogenesis-related differential genes were significantly correlated with TME. The constructed prognostic nomogram risk model predicted performance better and provided valuable information for immunotherapy. Multivariate Cox regression analysis and K-M analysis showed that ZC3H12A was a differentially expressed gene affecting the prognosis of SKCM and promoted the infiltration of anti-tumor immune cells CD8 + T cells, B cells, and DC cells. Based on the analysis of the protein interaction network method, 43 drugs were found to have high potential in the treatment of SKCM, and the literature search of these 43 drugs was carried out, and 21 drugs were found to have experimental verification for the treatment of SKCM. Taken together, the differential genes associated with the pathogenesis of SKCM have important roles in the tumor immune microenvironment, clinicopathological features, and prognosis, especially ZC3H12A has a potential role in identifying early SKCM patients. At the same time, it provides a new strategy for the drug development of SKCM and provides a basis for the reuse of SKCM drugs.

Open a new epoch of arsenic trioxide investigation: ATOdb

Arsenic trioxide (ATO) is a great discovery in the treatment of acute promyelocytic leukemia (APL), which has been used in an increasing number of malignant diseases. Systematic integrative analysis will help to precisely understand the mechanism of ATO and find new combined drugs. Therefore, we developed a one-stop comprehensive database of ATO named ATOdb by manually compiling a wealth of experimentally supported ATO-related data from 3479 articles, and integrated analysis tools. The current version of ATOdb contains 8373 associations among 2300 ATO targets, 80 conditions and 262 combined drugs. Each entry in ATOdb contains detailed information on ATO targets, therapeutic/side effects, systems, cell names, cell types, regulations, detection methods, brief descriptions, references, etc. Furthermore, ATOdb also provides data visualization and analysis results such as the drug similarities, protein-protein interactions, and miRNA-mRNA relationships. An easy-to-use web interface was deployed in ATOdb for users to easily browse, search and download the data. In conclusion, ATOdb will serve as a valuable resource for in-depth study of the mechanism of ATO, discovery of new drug combination strategies, promotion of rational drug use and individualized treatments. ATOdb is freely available at http://bio-bigdata.hrbmu.edu.cn/ATOdb/index.jsp.

The convergent evolution of influenza A virus: Implications, therapeutic strategies and what we need to know

Influenza virus infection, more commonly known as the 'cold flu', is an etiological agent that gives rise to recurrent annual flu and many pandemics. Dated back to the 1918- Spanish Flu, the influenza infection has caused the loss of many human lives and significantly impacted the economy and daily lives. Influenza virus can be classified into four different genera: influenza A-D, with the former two, influenza A and B, relevant to humans. The capacity of antigenic drift and shift in Influenza A has given rise to many novel variants, rendering vaccines and antiviral therapies useless. In light of the emergence of a novel betacoronavirus, the SARS-CoV-2, unravelling the underpinning mechanisms that support the recurrent influenza epidemics and pandemics is essential. Given the symptom similarities between influenza and covid infection, it is crucial to reiterate what we know about the influenza infection. This review aims to describe the origin and evolution of influenza infection. Apart from that, the risk factors entail the implication of co-infections, especially regarding the COVID-19 pandemic is further discussed. In addition, antiviral strategies, including the potential of drug repositioning, are discussed in this context. The diagnostic approach is also critically discussed in an effort to understand better and prepare for upcoming variants and potential influenza pandemics in the future. Lastly, this review encapsulates the challenges in curbing the influenza spread and provides insights for future directions in influenza management.

Repurposing miconazole and tamoxifen for the treatment of Mycobacterium abscessus complex infections through in silico chemogenomics approach

Drug repositioning is an alternative to overcome the complexity of the drug discovery and approval procedures for the treatment of Mycobacterium abscessus Complex (MABSC) infections that are increasing globally due to the emergency of antimicrobial resistance mechanisms. Here, an in silico chemogenomics approach was performed to compare the sequences from 4942 M. abscessus subsp. abscessus (M. abscessus) proteins with 5258 or 3473 therapeutic targets registered in the DrugBank or Therapeutic Target Database, respectively. This comparison identified 446 drugs or drug candidates whose targets were homologous to M. abscessus proteins. These identified drugs were considered potential inhibitors of MABSC (anti-MABSC activity). Further screening and inspection resulted in the selection of ezetimibe, furosemide, itraconazole, miconazole (MCZ), tamoxifen (TAM), and thiabendazole (THI) for experimental validation. Among them, MCZ and TAM showed minimum inhibitory concentrations (MIC) of 32 and 24 µg mL-1 against M. abscessus, respectively. For M. bolletii and M. massiliense strains, MCZ and TAM showed MICs of 16 and 24 µg mL-1, in this order. Subsequently, the antibacterial activity of MCZ was confirmed in vivo, indicating its potential to reduce the bacterial load in the lungs of infected mice. These results show that MCZ and TAM can serve as molecular scaffolds for the prospective hit-2-lead optimization of new analogs with greater potency, selectivity, and permeability.

In silico and in vitro analysis of the mechanisms of action of nitroxoline against some medically important opportunistic fungi

The increasing resistance to antifungal agents associated with toxicity and interactions turns therapeutic management of fungal infections difficult. This scenario emphasizes the importance of drug repositioning, such as nitroxoline - a urinary antibacterial agent that has shown potential antifungal activity. The aims of this study were to discover the possible therapeutic targets of nitroxoline using an in silico approach, and to determine the in vitro antifungal activity of the drug against the fungal cell wall and cytoplasmic membrane. We explored the biological activity of nitroxoline using PASS, SwissTargetPrediction and Cortellis Drug Discovery Intelligence web tools. After confirmation, the molecule was designed and optimized in HyperChem software. GOLD 2020.1 software was used to predict the interactions between the drug and the target proteins. In vitro investigation evaluated the effect of nitroxoline on the fungal cell wall through sorbitol protection assay. Ergosterol binding assay was carried out to assess the effect of the drug on the cytoplasmic membrane. In silico investigation revealed biological activity with alkane 1-monooxygenase and methionine aminopeptidase enzymes, showing nine and five interactions in the molecular docking, respectively. In vitro results exhibited no effect on the fungal cell wall or cytoplasmic membrane. Finally, nitroxoline has potential as an antifungal agent due to the interaction with alkane 1-monooxygenase and methionine aminopeptidase enzymes, which are not the main human therapeutic targets. These results have potentially revealed a new biological target for the treatment of fungal infections. We also consider that further studies are required to confirm the biological activity of nitroxoline on fungal cells, mainly the confirmation of the alkB gene.

Virtual screening reveals aprepitant to be a potent inhibitor of neutral sphingomyelinase 2: implications in blockade of exosome release in cancer therapy

PURPOSE

Exosomes are membrane-derived nano-vesicles upregulated in pathological conditions like cancer. Therefore, inhibiting their release is a potential strategy for the development of more efficient combination therapies. Neutral sphingomyelinase 2 (nSMase2) is a key component in exosome release; however, a clinically safe yet efficient nSMase2 inhibitor remains to be used discovered. Accordingly, we made an effort to identify potential nSMase2 inhibitor(s) among the approved drugs.

METHODS

Virtual screening was performed and aprepitant was selected for further investigation. To evaluate the reliability of the complex, molecular dynamics were performed. Finally, using the CCK-8 assay in HCT116 cells, the highest non-toxic concentrations of aprepitant were identified and the nSMase2 activity assay was performed to measure the inhibitory activity of aprepitant, in vitro.

RESULTS

To validate the screening results, molecular docking was performed, and the retrieved scores were in line with the screening results. The root-mean-square deviation (RMSD) plot of aprepitant-nSMase2 showed proper convergence. Following treatment with different concentrations of aprepitant in both cell-free and cell-dependent assays, nSMase2 activity was remarkably decreased.

CONCLUSION

Aprepitant, at a concentration as low as 15 µM, was able to inhibit nSmase2 activity in HCT116 cells without any significant effects on their viability. Aprepitant is therefore suggested to be a potentially safe exosome release inhibitor.

Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Aizheimer's disease using network pharmacology approach and in vitro experimental validation

Aims

To explore the new indications and key mechanism of Bazi Bushen capsule (BZBS) by network pharmacology and in vitro experiment.

Methods

The ingredients library of BZBS was constructed by retrieving multiple TCM databases. The potential target profiles of the components were predicted by target prediction algorithms based on different principles, and validated by using known activity data. The target spectrum of BZBS with high reliability was screened by considering the source of the targets and the node degree in compound-target (C-T) network. Subsequently, new indications for BZBS were predicted by disease ontology (DO) enrichment analysis and initially validated by GO and KEGG pathway enrichment analysis. Furthermore, the target sets of BZBS acting on AD signaling pathway were identified by intersection analysis. Based on STRING database, the PPI network of target was constructed and their node degree was calculated. Two Alzheimer's disease (AD) cell models, BV-2 and SH-SY5Y, were used to preliminarily verify the anti-AD efficacy and mechanism of BZBS in vitro.

Results

In total, 1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula, and 1320 BZBS targets with high confidence were predicted. Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treatment of AD. GO and KEGG enrichment results provide a preliminary verification of this point. Among them, 113 functional targets of BZBS belong to AD pathway. A PPI network containing 113 functional targets and 1051 edges for the treatment of AD was constructed. In vitro experiments showed that BZBS could significantly reduce the release of TNF-α and IL-6 and the expression of COX-2 and PSEN1 in Aβ_25-35-induced BV-2 cells, which may be related to the regulation of ERK_1/2/NF-κB signaling pathway. BZBS reduced the apoptosis rate of Aβ_25-35 induced SH-SY5Y cells, significantly increased mitochondrial membrane potential, reduced the expression of Caspase3 active fragment and PSEN1, and increased the expression of IDE. This may be related to the regulation of GSK-3β/β-catenin signaling pathway.

Conclusions

BZBS formula has a potential use in the treatment of AD, which is achieved through regulation of ERK_1/2, NF-κB signaling pathways, and GSK-3β/β-catenin signaling pathway. Furthermore, the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action. The study lays a foundation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.

Estimation of contribution of CYP2D6 to tipepidine metabolism in humans and prolongation of the half-life of tipepidine by combination use with a CYP2D6 inhibitor in chimeric mice with humanized liver

1. Recently, it has been reported that tipepidine has various central pharmacological effects and can be expected to be safely repositioned as treatment for psychiatric disorders. Since tipepidine has a very short half-life and requires three doses per day, development of a once-daily medication would be highly beneficial to improve adherence and quality of life in patients with chronic psychiatric disorders. The aim of this study was to identify the enzymes involved in tipepidine metabolism and to verify that combination use with an enzyme inhibitor prolongs the half-life of tipepidine.2. Metabolism studies using recombinant human cytochrome P450 (P450, CYP) isoforms and inhibition studies using various selective P450 inhibitors and human liver microsomes revealed that CYP2D6 is the main enzyme catalyzing tipepidine metabolism, with a metabolic contribution ratio of 85.4%.3. Furthermore, pharmacokinetic study using chimeric mice with humanized liver showed that oral coadministration of a CYP2D6 inhibitor, quinidine, increased the C_max, AUC_0-t, and t_1/2 of tipepidine by 1.5-, 3.2-, and 3.0-fold, respectively.4. These results indicated that coadministration of a CYP2D6 inhibitor is effective in increasing the plasma exposure and prolonging the half-life of tipepidine and is useful for repositioning tipepidine as treatment for psychiatric disorders.

A phase II study of metformin plus pemetrexed and carboplatin in patients with non-squamous non-small cell lung cancer (METALUNG)

Immune checkpoint inhibitors (ICIs) ± chemotherapy is the standard treatment for driver mutation-negative non-small cell lung cancer (NSCLC). However, accessibility to ICIs in LMICs is limited due to high cost, and platinum-based chemotherapy remains the mainstay of treatment. Metformin has anticancer properties, and studies suggest synergism between metformin and pemetrexed. Based on preclinical evidence, this combination may be more beneficial for STK11-mutated NSCLC, a subgroup, inherently resistant to ICIs. In this Simon two-stage, single-arm phase 2 trial, we investigated metformin with pemetrexed-carboplatin (PC) in patients with treatment-naive stage IV non-squamous NSCLC. The primary outcome was 6-month progression-free survival (PFS) rate. Secondary outcomes were safety, overall survival (OS), overall response rate (ORR), proportion of STK11 mutation, and effect of STK11 mutation on 6-month PFS rate. The study was terminated for futility after interim analysis. The median follow-up was 34.1 months. The 6-month PFS rate was 28% (95% CI 12.4-0.46). The median PFS and OS were 4.5 (95% CI 2.2-6.1) and 7.4 months (95% CI 5.3-15.3), respectively. The ORR was 72%. Gastrointestinal toxicities were the most common. No grade 4/5 toxicities were reported. Targeted sequencing was possible in nine cases. Two patients had STK11 mutation and a poor outcome (PFS < 12 weeks). We could not demonstrate the benefit of metformin with CP in terms of improvement in 6-month PFS rate; however, the combination was safe (CTRI/2019/02/017815).

Drug repositioning of disulfiram induces endometrioid epithelial ovarian cancer cell death via the both apoptosis and cuproptosis pathways

Various therapeutic strategies have been developed to overcome ovarian cancer. However, the prognoses resulting from these strategies are still unclear. In the present work, we screened 54 small molecule compounds approved by the FDA to identify novel agents that could inhibit the viability of human epithelial ovarian cancer cells. Among these, we identified disulfiram (DSF), an old alcohol-abuse drug, as a potential inducer of cell death in ovarian cancer. Mechanistically, DSF treatment significantly reduced the expression of the anti-apoptosis marker B-cell lymphoma/leukemia-2 (Bcl-2) and increase the expression of the apoptotic molecules Bcl2 associated X (Bax) and cleaved caspase-3 to promote human epithelial ovarian cancer cell apoptosis. Furthermore, DSF is a newly identified effective copper ionophore, thus the combination of DSF and copper was used to reduce ovarian cancer viability than DSF single treatment. Combination treatment with DSF and copper also led to the reduced expression of ferredoxin 1 and loss of Fe-S cluster proteins (biomarkers of cuproptosis). In vivo, DSF and copper gluconate significantly decreased the tumor volume and increased the survival rate in a murine ovarian cancer xenograft model. Thus, the role of DSF revealed its potential for used as a viable therapeutic agent for the ovarian cancer.

Assessing nystatin cream treatment efficacy against Leishmania (L.) amazonensis infection in BALB/c model

The current scenario for cutaneous leishmaniasis treatment includes the use of first and second-choice drugs, both therapeutic strategies presenting several adverse effects and being related to an increment of treatment-refractory parasite strains. These facts encourage the search for new treatment approaches, including repositioning drugs, such as nystatin. Although some in vitro assays show that this polyene macrolide compound has leishmanicidal activity, no in vivo evidence for a similar activity has been shown so far for the commercial nystatin cream formulation (25,000 IU/g). This work assessed the effects of nystatin cream administered on mice in an amount to completely cover the paw surface of BALB/c mice infected with L. (L.) amazonensis once a day, until a total of up to 20 doses. The data presented herein points to unequivocal evidence that this formulation is related to a statistically significant reduction of swelling/edema in mice paws when compared to animal groups not submitted to this treatment regimen after the fourth week of infection: lesion sizes at the sixth (p = 0.0159), seventh (p = 0.0079) and eighth (p = 0.0079) week. Furthermore, infection reduction relates to a decrease in parasite load in the footpad (∼48%) and in draining lymph nodes (∼68%) at 8th weeks post-infection. This is the first report of the effectiveness of nystatin cream used as a topical treatment in BALB/c model for cutaneous leishmaniasis.

Epigenomic and transcriptomic landscaping unraveled candidate repositioned therapeutics for non-functioning pituitary neuroendocrine tumors

PURPOSE

Non-functioning pituitary neuroendocrine tumors are challengingly diagnosed tumors in the clinic. Transsphenoidal surgery remains the first-line treatment. Despite the development of state-of-the-art techniques, no drug therapy is currently approved for the treatment. There are also no randomized controlled trials comparing therapeutic strategies or drug therapy for the management after surgery. Therefore, novel therapeutic interventions for the therapeutically challenging NF-PitNETs are urgently needed.

METHODS

We integrated epigenome and transcriptome data (both coding and non-coding) that elucidate disease-specific signatures, in addition to biological and pharmacological data, to utilize rational pathway and drug prioritization in NF-PitNETs. We constructed an epigenome- and transcriptome-based PPI network and proposed hub genes. The signature-based drug repositioning based on the integration of multi-omics data was performed.

RESULTS

The construction of a disease-specific network based on three different biological levels revealed DCC, DLG5, ETS2, FOXO1, HBP1, HMGA2, PCGF3, PSME4, RBPMS, RREB1, SMAD1, SOCS1, SOX2, YAP1, ZFHX3 as hub proteins. Signature-based drug repositioning using hub proteins yielded repositioned drug candidates that were confirmed in silico via molecular docking. As a result of molecular docking simulations, palbociclib, linifanib, trametinib, eplerenone, niguldipine, and zuclopenthixol showed higher binding affinities with hub genes compared to their inhibitors and were proposed as potential repositioned therapeutics for the management of NF-PitNETs.

CONCLUSION

The proposed systems' biomedicine-oriented multi-omics data integration for drug repurposing to provide promising results for the construction of effective clinical therapeutics. To the best of our knowledge, this is the first study reporting epigenome- and transcriptome-based drug repositioning for NF-PitNETs using in silico confirmations.

Exploring drug repositioning for leishmaniasis treatment: Ivermectin plus polymeric micelles induce immunological response and protection against tegumentary leishmaniasis

Leishmania amazonensis can cause a wide spectrum of the clinical manifestations of leishmaniasis in humans. The development of new therapeutics is a long and expensive task; in this context, drug repositioning could be considered a strategy to identify new biological actions of known products. In the present study, ivermectin (IVE) was tested against distinct Leishmania species able to cause disease in humans. In vitro experiments showed that IVE was effective to reduce the infection degree and parasite load in Leishmania donovani- and L. amazonensis-infected macrophages that were treated with it. In addition, using the culture supernatant of treated macrophages, higher production of IFN-γ and IL-12 and lower levels of IL-4 and IL-10 were found. Then, IVE was used in a pure form or incorporated into Poloxamer 407-based polymeric micelles (IVE/M) for the treatment of L. amazonensis-infected BALB/c mice. Animals (n = 16 per group) were infected and later received saline, empty micelles, amphotericin B (AmpB), IVE, or IVE/M. They were euthanized at one (n = 8 per group) and 30 (n = 8 per group) days after treatment and, in both endpoints, immunological, parasitological, and biochemical evaluations were performed. Results showed that both IVE and IVE/M induced higher levels of IFN-γ, IL-12, GM-CSF, nitrite, and IgG2a antibodies, as well as higher IFN-γ expression evaluated by RT-qPCR in spleen cell cultures. Such animals showed low organic toxicity, as well as significant reductions in the lesion's average diameter and parasite load in their infected tissue, spleen, liver, and draining lymph node. The efficacy was maintained 30 days post-therapy, while control mice developed a polarized Th2-type response and high parasite load. In this context, IVE could be considered as a new candidate to be applied in future studies for the treatment against distinct Leishmania species.

A compact review of progress and prospects of deep learning in drug discovery

BACKGROUND

Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development.

RESULTS

This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.

Effects of antibacterial agents on cancerous cell proliferation

Myelosuppression, a side effect of anticancer drugs, makes people more susceptible to infectious diseases by compromising the immune system. When a cancer patient develops a contagious disease, treatment with an anticancer drug is suspended or postponed to treat the infectious disease. If there were a drug that suppresses the growth of cancer cells among antibacterial agents, it would be possible to treat both infectious diseases and cancer. Therefore, this study investigated the effect of antibacterial agents on cancer cell development. Vancomycin (VAN) had little effect on cell proliferation against the breast cancer cell, MCF-7, prostate cancer cell, PC-3, and gallbladder cancer cell, NOZ C-1. Alternatively, Teicoplanin (TEIC) and Daptomycin (DAP) promoted the growth of some cancer cells. In contrast, Linezolid (LZD) suppressed the proliferation of MCF-7, PC-3, and NOZ C-1 cells. Therefore, we found a drug that affects the growth of cancer cells among antibacterial agents. Next, when we examined the effects of the combined use of existing anticancer and antibacterial agents, we found VAN did not affect the growth suppression by anticancer agents. However, TEIC and DAP attenuated the growth suppression of anticancer agents. In contrast, LZD additively enhanced the growth suppression by Docetaxel in PC-3 cells. Furthermore, we showed that LZD inhibits cancer cell growth by mechanisms that involve PI3K/Akt pathway suppression. Therefore, LZD might simultaneously treat cancer and infectious diseases.

Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer

Breast cancer is one of the leading causes of death in women worldwide. Initially, it develops in the epithelium of the ducts or lobules of the breast glandular tissues with limited growth and the potential to metastasize. It is a highly heterogeneous malignancy; however, the common molecular mechanisms could help identify new targeted drugs for treating its subtypes. This study uses computational drug repositioning approaches to explore fresh drug candidates for breast cancer treatment. We also implemented reversal gene expression and gene expression-based signatures to explore novel drug candidates computationally. The drug activity profiles and related gene expression changes were acquired from the DrugBank, PubChem, and LINCS databases, and then in silico drug screening, molecular dynamics (MD) simulation, replica exchange MD simulations, and simulated annealing molecular dynamics (SAMD) simulations were conducted to discover and verify the valid drug candidates. We have found that compounds like furosemide, gold, and dopamine showed significant outcomes. Furthermore, the expression of genes related to breast cancer was observed to be reversed by these shortlisted drugs. Therefore, we postulate that combining furosemide, gold, and dopamine would be a potential combination therapy measurement for breast cancer patients.

Real-world evidence for beneficial effects of dipyrone in 4,278 patients with pulmonary hypertension - Analysis of the risk of ventilation, hospitalization, and agranulocytosis

INTRODUCTION

Dipyrone has been used as an analgesic for a century, but recently was proposed as a novel therapeutic strategy for the prevention and therapy of pulmonary hypertension (PH). The aim of this study was to analyze whether the risk for ventilation procedures and hospitalization was lower among patients with PH who used dipyrone compared to subjects who did not use dipyrone.

MATERIALS AND METHODS

Initially, patients with PH were retrieved from the TriNetX database, whereby subjects who used dipyrone were assigned to cohort I, and cohort II was formed by those individuals who did not use dipyrone. Both cohorts were matched for several variables. The outcomes were requirement for ventilation procedures and hospital admission, whereby the time window to record events was 5 years after diagnosis of PH. Subsequently, risk analysis was carried out, and risk ratio (RR) and odds ratio (OR) were calculated. In addition, the risk of agranulocytosis was determined for both cohorts.

RESULTS

Out of 741,875 individuals diagnosed with PH 4,282 and 737,593 patients were assigned to the cohorts I and II. After matching, each cohort accounted for 4,278 individuals. Among the cohorts I and II 10 and 187 individuals required ventilation procedures. The according risks of 0.2% vs. 4.4% were significantly different (p < 0.0001; Log-Rank test). RR and OR were 0.053 and 0.051. Within the cohorts I and II 10 and 1,195 subjects required hospital admission. The risks of hospitalization of 0,4% vs. 27.9% differed significantly (p < 0.0001). RR and OR were 0.016 and 0.012. Among the cohorts I and II 47 and 66 individuals were diagnosed with agranulocytosis, whereby no significance was found (p > 0.05).

CONCLUSIONS

The risk for ventilation measures and hospitalization among patients with PH was found to be significantly lower when dipyrone was used. Even though the underlying mechanisms remain unknown to date, they are supposedly mediated by an active metabolite of dipyrone. The obtained results appear to be promising for patients suffering from PH. Hence, the present study may encourage further research.

Valproate and lithium: Old drugs for new pharmacological approaches in brain tumors?

Beyond its use as an antiepileptic drug, over time valproate has been increasingly used for several other therapeutic applications. Among these, the antineoplastic effects of valproate have been assessed in several in vitro and in vivo preclinical studies, suggesting that this agent significantly inhibits cancer cell proliferation by modulating multiple signaling pathways. During the last years various clinical trials have tried to find out if valproate co-administration could enhance the antineoplastic activity of chemotherapy in glioblastoma patients and in patients suffering from brain metastases, demonstrating that the inclusion of valproate in the therapeutic schedule causes an improved median overall survival in some studies, but not in others. Thus, the effects of the use of concomitant valproate in brain cancer patients are still controversial. Similarly, lithium has been tested as an anticancer drug in several preclinical studies mainly using the unregistered formulation of lithium chloride salts. Although, there are no data showing that the anticancer effects of lithium chloride are superimposable to the registered lithium carbonate, this formulation has shown preclinical activity in glioblastoma and hepatocellular cancers. However, few but interesting clinical trials have been performed with lithium carbonate on a very small number of cancer patients. Based on published data, valproate could represent a potential complementary therapeutic approach to enhance the anticancer activity of brain cancer standard chemotherapy. Same advantageous characteristics are less convincing for lithium carbonate. Therefore, the planning of specific phase III studies is necessary to validate the repositioning of these drugs in present and future oncological research.

Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients.

METHODS

Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins.

FINDINGS

We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments.

INTERPRETATION

We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.

Transcriptomic screening to identify hub genes and drug signatures for PCOS based on RNA-Seq data in granulosa cells

BACKGROUND

Polycystic ovary syndrome (PCOS) is one of the most incident reproductive diseases, and remains the main cause of female infertility. Granulosa cells play a critical role in normal follicle development and steroid hormones synthesis. In spite of extensive research, no sole medication has been approved by FDA to treat PCOS. This study aimed to investigate the novel therapeutics targets in PCOS, focusing on granulosa cells transcriptome functional analysis with a drug repositioning approach.

METHODS

PCOS microarray and RNA-Seq datasets in granulosa cells were screened and reanalyzed. KEGG pathway enrichment and interaction network analyses were performed and followed by a set of drug signature screening and Poly-pharmacology survey.

RESULTS

545 deregulated genes were identified via filters including p_adj < 0.05 and |log2FC| > 1. Amongst the top 15 KEGG pathways significantly enriched, metabolism of xenobiotics by cytochrome P450, steroid hormone biosynthesis and ovarian steroidogenesis were observed. The Protein-Protein Interaction network identified 18 hub genes amongst this set. Interestingly, most candidate drug signatures have been introduced by databases are either FDA approved or entered into clinical trials, including melatonin, resveratrol and raloxifene. Investigational or experimental introduced drugs obey rules of drug-likeness with almost safe and acceptable ADMET properties. Notably, 21 top target genes of the final drug set were also included in the granulosa significant differentially expressed genes.

CONCLUSION

Results of the current study represent approved, investigational and experimental drug signatures according to the differentially expressed genes in granulosa cells with supported literature reviews. This data might be useful for researchers and clinicians to pave the way for better management of PCOS.