Comput Biol Med. 2023 Sep 28;166:107496. doi: 10.1016/j.compbiomed.2023.107496. Online ahead of print.

ABSTRACT

The progressive emergence of antimicrobial resistance has become a global health problem in need of rapid solution. Research into new antimicrobial drugs is imperative. Drug repositioning, together with computational mathematical prediction models, could be a fast and efficient method of searching for new antibiotics. The aim of this study was to identify compounds with potential antimicrobial capacity against Escherichia coli from US Food and Drug Administration-approved drugs, and the similarity between known drug targets and E. coli proteins using a topological structure-activity data analysis model. This model has been shown to identify molecules with known antibiotic capacity, such as carbapenems and cephalosporins, as well as new molecules that could act as antimicrobials. Topological similarities were also found between E. coli proteins and proteins from different bacterial species such as Mycobacterium tuberculosis, Pseudomonas aeruginosa and Salmonella Typhimurium, which could imply that the selected molecules have a broader spectrum than expected. These molecules include antitumor drugs, antihistamines, lipid-lowering agents, hypoglycemic agents, antidepressants, nucleotides, and nucleosides, among others. The results presented in this study prove the ability of computational mathematical prediction models to predict molecules with potential antimicrobial capacity and/or possible new pharmacological targets of interest in the design of new antibiotics and in the better understanding of antimicrobial resistance.

PMID:37793206 | DOI:10.1016/j.compbiomed.2023.107496

Front Genet. 2023 Sep 18;14:1170571. doi: 10.3389/fgene.2023.1170571. eCollection 2023.

ABSTRACT

Pancreatic Ductal Adenocarcinoma (PDAC) is a very lethal disease that typically presents at an advanced stage and is non-compliant with most treatments. Recent technologies have helped delineate associated molecular subtypes and genetic variations yielding important insights into the pathophysiology of this disease and having implications for the identification of new therapeutic targets. Drug repurposing has been evaluated as a new paradigm in oncology to accelerate the application of approved or failed target-specific molecules for the treatment of cancer patients. This review focuses on the impact of molecular subtypes on key genomic alterations in PDAC, and the progress made thus far. Importantly, these alterations are discussed in light of the potential role of drug repurposing in PDAC.

PMID:37790705 | PMC:PMC10544984 | DOI:10.3389/fgene.2023.1170571

PeerJ. 2023 Sep 29;11:e16088. doi: 10.7717/peerj.16088. eCollection 2023.

ABSTRACT

BACKGROUND: Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions.

METHODS: We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication.

RESULTS: As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

PMID:37790614 | PMC:PMC10544355 | DOI:10.7717/peerj.16088

Res Sq. 2023 Sep 12:rs.3.rs-3335643. doi: 10.21203/rs.3.rs-3335643/v1. Preprint.

ABSTRACT

Background Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. Methods We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. Results We identified 316 dysregulated LR interactions across six major cell types in AD PFC, of which 210 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 60 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport', among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. Conclusions Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

PMID:37790454 | PMC:PMC10543294 | DOI:10.21203/rs.3.rs-3335643/v1

bioRxiv. 2023 Sep 24:2023.09.22.559001. doi: 10.1101/2023.09.22.559001. Preprint.

ABSTRACT

Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. We observe that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue, as well as in tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also uncovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS . Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

PMID:37790409 | PMC:PMC10543009 | DOI:10.1101/2023.09.22.559001

BMC Bioinformatics. 2023 Oct 3;24(1):374. doi: 10.1186/s12859-023-05479-7.

ABSTRACT

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 .

PMID:37789314 | DOI:10.1186/s12859-023-05479-7

Curr Health Sci J. 2023 Apr-Jun;49(2):210-219. doi: 10.12865/CHSJ.49.02.210. Epub 2023 Jun 30.

ABSTRACT

Stroke is a major cause of death and disability worldwide. Between 1990 and 2010, its global burden increased notably with reference to the absolute number of incident events, number of deaths, and disability-adjusted life-years lost. Trazodone is a triazolopyridine derivative that was approved for more than 40 years as monotherapy or in combination with other antidepressant drugs for the treatment of major depressive disorder in adult patients. The aim was investigated if trazodone can improve behavioural outcome after stroke in a mice model of middle cerebral artery occlusion (MCAo) due to the potential neuroprotective and neurodegenerative effects by using three behavioural tests: adhesive tape test, beam test and hole board test. Trazodone administration show modest improvements regarding the motor-sensorial function after stroke especially in the acute post-stroke phase in aged and young animals. The antidepressant effect of the drug was observed in the post-stroke period in aged animals and to a lesser extent in young animals. Future research is needed to evaluate the effects of trazodone at the cellular level to be sure that it has no benefit in stroke patients who do not suffer from depression.

PMID:37786617 | PMC:PMC10541511 | DOI:10.12865/CHSJ.49.02.210

Arch Biochem Biophys. 2023 Sep 30:109770. doi: 10.1016/j.abb.2023.109770. Online ahead of print.

ABSTRACT

Angiotensin receptor blockers (ARBs) have been reported to be beneficial of renal fibrosis, but the molecular and cellular mechanisms are still unclear. In this study, we investigated the effectiveness and relevant mechanism of ARBs in alleviating renal fibrosis, especially by focusing on biomechanical stress-induced epithelial to mesenchymal transition (EMT) of renal epithelial cells. Unilateral ureteral obstruction (UUO) renal fibrosis model was established in mice by ligating the left ureter, and then randomly received losartan at a low dose (1 mg/kg) or a regular dose (3 mg/kg) for 2 weeks. Compared to the control, histological analysis showed that losartan treatment at either a low dose or a regular dose effectively attenuated renal fibrosis in the UUO model. To further understand the mechanism, we ex vivo loaded primary human renal epithelial cells to 50 mmHg hydrostatic pressure. Western blot and immunostaining analyses indicated that the loading to 50 mmHg hydrostatic pressure for 24 h significantly upregulated vimentin, β-catenin and α-SMA, but downregulated E-cadherin in renal epithelial cells, suggesting the EMT. The addition of 10 or 100 nM losartan in medium effectively attenuated the EMT of renal epithelial cells induced by 50 mmHg hydrostatic pressure loading. Our in vivo and ex vivo experimental data suggest that losartan treatment, even at a low dose can effectively alleviate renal fibrosis in mouse UUO model, at least partly by inhibiting the biomechanical stress-induced EMT of renal epithelial cells. A low dose of ARBs may repurpose for renal fibrosis treatment.

PMID:37783367 | DOI:10.1016/j.abb.2023.109770

ACS Omega. 2023 Sep 15;8(38):34583-34596. doi: 10.1021/acsomega.3c03471. eCollection 2023 Sep 26.

ABSTRACT

Cancer remains a major cause of morbidity and mortality worldwide, and while current therapies, such as chemotherapy, immunotherapy, and cell therapy, have been effective in many patients, the development of novel therapeutic options remains an urgent priority. Mouse double minute 2 (MDM2) is a key regulator of the tumor suppressor protein p53, which plays a critical role in regulating cellular growth, apoptosis, and DNA repair. Consequently, MDM2 has been the subject of extensive research aimed at developing novel cancer therapies. In this study, we employed a machine learning-based approach to establish a quantitative structure-activity relationship model capable of predicting the potential in vitro efficacy of small molecules as MDM2 inhibitors. Our model was used to screen 5883 FDA-approved drugs, resulting in the identification of promising hits that were subsequently evaluated using molecular docking and molecular dynamics simulations. Two antihistamine drugs, cetirizine (CZ) and rupatadine (RP), exhibited particularly favorable results in the initial in silico analyses. To further assess their potential use as the activators of the p53 pathway, we investigated the antiproliferative capability of the abovementioned drugs on human glioblastoma and neuroblastoma cell lines. Both the compounds exhibited significant antiproliferative effects on the abovementioned cell lines in a dose-dependent manner. The half-maximal inhibitory concentration (IC50) of CZ was found to be 697.87 and 941.37 μM on U87 and SH-SY5Y cell lines, respectively, while the IC50 of RP was found to be 524.28 and 617.07 μM on the same cell lines, respectively. Further investigation by quantitative reverse transcriptase polymerase chain reaction analysis revealed that the CZ-treated cell lines upregulate the expression of the p53-regulated genes involved in cell cycle arrest, apoptosis, and DNA damage response compared to their respective vehicle controls. These findings suggest that CZ activates the p53 pathway by inhibiting MDM2. Our results provide compelling preclinical evidence supporting the potential use of CZ as a modulator of the MDM2-p53 axis and its plausible repurposing for cancer treatment.

PMID:37779953 | PMC:PMC10536845 | DOI:10.1021/acsomega.3c03471

Cureus. 2023 Aug 30;15(8):e44359. doi: 10.7759/cureus.44359. eCollection 2023 Aug.

ABSTRACT

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

PMID:37779744 | PMC:PMC10539991 | DOI:10.7759/cureus.44359

Diabetes Obes Metab. 2023 Oct 1. doi: 10.1111/dom.15298. Online ahead of print.

ABSTRACT

AIM: Genome-wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)-relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR-relevant phenotypes via a transcriptome-wide association study.

MATERIALS AND METHODS: We conducted a large-scale multi-tissue transcriptome-wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment-IR, fasting insulin) and lipid-relevant traits (high-density lipoprotein cholesterol, triglycerides, low-density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis.

RESULTS: We identified 1190 susceptibility genes causally associated with IR-relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR-related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR-related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk.

CONCLUSIONS: Our study provided new insights into IR aetiology and avenues for therapeutic development.

PMID:37779362 | DOI:10.1111/dom.15298

Methods. 2023 Sep 29:S1046-2023(23)00164-0. doi: 10.1016/j.ymeth.2023.09.010. Online ahead of print.

ABSTRACT

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to discover and develop innovative drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. For example, the AI-powered platform ATOM developed by Atom wise can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure- and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

PMID:37778659 | DOI:10.1016/j.ymeth.2023.09.010

BMC Bioinformatics. 2023 Sep 30;24(1):367. doi: 10.1186/s12859-023-05497-5.

ABSTRACT

BACKGROUND: Obtaining accurate drug-target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive.

RESULTS: Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA .

CONCLUSIONS: The experimental results show that DGDTA can predict DTA more accurately than some other methods.

PMID:37777712 | DOI:10.1186/s12859-023-05497-5

Med Oncol. 2023 Sep 30;40(11):312. doi: 10.1007/s12032-023-02183-7.

ABSTRACT

Immunotherapies are promising therapeutic options for the management of triple-negative breast cancer because of its high mutation rate and genomic instability. Of note, the blockade of the immune checkpoint protein PD-1 and its ligand PD-L1 has been proven to be an efficient and potent strategy to combat triple-negative breast cancer. To date, various anti-PD-1/anti-PD-L1 antibodies have been approved. However, the intrinsic constraints of these therapeutic antibodies significantly limit their application, making small molecules a potentially significant option for PD-1/PD-L1 inhibition. In light of this, the current study aims to use a high-throughput virtual screening technique to identify potential repurposed candidates as PD-L1 inhibitors. Thus, the present study explored binding efficiency of 2509 FDA-approved compounds retrieved from the drug bank database against PD-L1 protein. The binding affinity of the compounds was determined using the glide XP docking programme. Furthermore, prime-MM/GBSA, DFT calculations, and RF score were used to precisely re-score the binding free energy of the docked complexes. In addition, the ADME and toxicity profiles for the lead compounds were also examined to address PK/PD characteristics. Altogether, the screening process identified three molecules, namely DB01238, DB06016 and DB01167 as potential therapeutics for the PD-L1 protein. To conclude, a molecular dynamic simulation of 100 ns was run to characterise the stability and inhibitory action of the three lead compounds. The results from the simulation study confirm the robust structural and thermodynamic stability of DB01238 than other investigated molecules. Thus, our findings hypothesize that DB01238 could serve as potential PD-L1 inhibitor in the near future for triple-negative breast cancer patients.

PMID:37777635 | DOI:10.1007/s12032-023-02183-7

J Comput Aided Mol Des. 2023 Sep 30. doi: 10.1007/s10822-023-00533-1. Online ahead of print.

ABSTRACT

Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.

PMID:37777631 | DOI:10.1007/s10822-023-00533-1

Sci Rep. 2023 Sep 30;13(1):16443. doi: 10.1038/s41598-023-43219-2.

ABSTRACT

Neuroblastoma, the most common type of pediatric extracranial solid tumor, causes 10% of childhood cancer deaths. Despite intensive multimodal treatment, the outcomes of high-risk neuroblastoma remain poor. We urgently need to develop new therapies with safe long-term toxicity profiles for rapid testing in clinical trials. Drug repurposing is a promising approach to meet these needs. Here, we investigated disulfiram, a safe and successful chronic alcoholism treatment with known anticancer and epigenetic effects. Disulfiram efficiently induced cell cycle arrest and decreased the viability of six human neuroblastoma cell lines at half-maximal inhibitory concentrations up to 20 times lower than its peak clinical plasma level in patients treated for chronic alcoholism. Disulfiram shifted neuroblastoma transcriptome, decreasing MYCN levels and activating neuronal differentiation. Consistently, disulfiram significantly reduced the protein level of lysine acetyltransferase 2A (KAT2A), drastically reducing acetylation of its target residues on histone H3. To investigate disulfiram's anticancer effects in an in vivo model of high-risk neuroblastoma, we developed a disulfiram-loaded emulsion to deliver the highly liposoluble drug. Treatment with the emulsion significantly delayed neuroblastoma progression in mice. These results identify KAT2A as a novel target of disulfiram, which directly impacts neuroblastoma epigenetics and is a promising candidate for repurposing to treat pediatric neuroblastoma.

PMID:37777587 | DOI:10.1038/s41598-023-43219-2

Nat Cardiovasc Res. 2023 Jun;2(6):550-571. doi: 10.1038/s44161-023-00278-y. Epub 2023 Jun 8.

ABSTRACT

The development of new immunotherapies to treat the inflammatory mechanisms that sustain atherosclerotic cardiovascular disease (ASCVD) is urgently needed. Herein, we present a path to drug repurposing to identify immunotherapies for ASCVD. The integration of time-of-flight mass cytometry and RNA sequencing identified unique inflammatory signatures in peripheral blood mononuclear cells stimulated with ASCVD plasma. By comparing these inflammatory signatures to large-scale gene expression data from the LINCS L1000 dataset, we identified drugs that could reverse this inflammatory response. Ex vivo screens, using human samples, showed that saracatinib-a phase 2a-ready SRC and ABL inhibitor-reversed the inflammatory responses induced by ASCVD plasma. In Apoe-/- mice, saracatinib reduced atherosclerosis progression by reprogramming reparative macrophages. In a rabbit model of advanced atherosclerosis, saracatinib reduced plaque inflammation measured by [18F] fluorodeoxyglucose positron emission tomography-magnetic resonance imaging. Here we show a systems immunology-driven drug repurposing with a preclinical validation strategy to aid the development of cardiovascular immunotherapies.

PMID:37771373 | PMC:PMC10538622 | DOI:10.1038/s44161-023-00278-y

ESMO Open. 2023 Sep 26;8(5):101642. doi: 10.1016/j.esmoop.2023.101642. Online ahead of print.

ABSTRACT

Precision medicine for cancer is rapidly moving to an approach that integrates multiple dimensions of the biology in order to model mechanisms of cancer progression in each patient. The discovery of multiple drivers per tumor challenges medical decision that faces several treatment options. Drug sensitivity depends on the actionability of the target, its clonal or subclonal origin and coexisting genomic alterations. Sequencing has revealed a large diversity of drivers emerging at treatment failure, which are potential targets for clinical trials or drug repurposing. To effectively prioritize therapies, it is essential to rank genomic alterations based on their proven actionability. Moving beyond primary drivers, the future of precision medicine necessitates acknowledging the intricate spatial and temporal heterogeneity inherent in cancer. The advent of abundant complex biological data will make artificial intelligence algorithms indispensable for thorough analysis. Here, we will discuss the advancements brought by the use of high-throughput genomics, the advantages and limitations of precision medicine studies and future perspectives in this field.

PMID:37769400 | DOI:10.1016/j.esmoop.2023.101642

Int J Mol Sci. 2023 Sep 7;24(18):13826. doi: 10.3390/ijms241813826.

ABSTRACT

The identification of novel drug targets is needed to improve the outcomes of heart failure (HF). G-protein-coupled receptors (GPCRs) represent the largest family of targets for already approved drugs, thus providing an opportunity for drug repurposing. Here, we aimed (i) to investigate the differential expressions of 288 cardiac GPCRs via droplet digital PCR (ddPCR) and bulk RNA sequencing (RNAseq) in a rat model of left ventricular pressure-overload; (ii) to compare RNAseq findings with those of ddPCR; and (iii) to screen and test for novel, translatable GPCR drug targets in HF. Male Wistar rats subjected to transverse aortic constriction (TAC, n = 5) showed significant systolic dysfunction vs. sham operated animals (SHAM, n = 5) via echocardiography. In TAC vs. SHAM hearts, RNAseq identified 69, and ddPCR identified 27 significantly differentially expressed GPCR mRNAs, 8 of which were identified using both methods, thus showing a correlation between the two methods. Of these, Prostaglandin-F2α-receptor (Ptgfr) was further investigated and localized on cardiomyocytes and fibroblasts in murine hearts via RNA-Scope. Antagonizing Ptgfr via AL-8810 reverted angiotensin-II-induced cardiomyocyte hypertrophy in vitro. In conclusion, using ddPCR as a novel screening method, we were able to identify GPCR targets in HF. We also show that the antagonism of Ptgfr could be a novel target in HF by alleviating cardiomyocyte hypertrophy.

PMID:37762130 | DOI:10.3390/ijms241813826

F1000Res. 2022 Mar 23;11:Chem Inf Sci-217. doi: 10.12688/f1000research.109017.2. eCollection 2022.

ABSTRACT

Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the "repurposing" concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.

PMID:37767081 | PMC:PMC10521104 | DOI:10.12688/f1000research.109017.2