Biomedicines. 2023 May 25;11(6):1531. doi: 10.3390/biomedicines11061531.

ABSTRACT

Human respiratory syncytial virus (hRSV) affects more than 33 million people each year, but there are currently no effective drugs or vaccines approved. In this study, we first constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via big-data mining. Then, we employed reversed dynamic methods via two-side host-pathogen RNA-seq time-profile data to prune false positives in candidate HPI-GWGEN to obtain the real HPI-GWGEN. With the aid of principal-network projection and the annotation of KEGG pathways, we can extract core signaling pathways during hRSV infection to investigate the pathogenic mechanism of hRSV infection and select the corresponding significant biomarkers as drug targets, i.e., TRAF6, STAT3, IRF3, TYK2, and MAVS. Finally, in order to discover potential molecular drugs, we trained a DNN-based DTI model by drug-target interaction databases to predict candidate molecular drugs for these drug targets. After screening these candidate molecular drugs by three drug design specifications simultaneously, i.e., regulation ability, sensitivity, and toxicity. We finally selected acitretin, RS-67333, and phenformin to combine as a potential multimolecule drug for the therapeutic treatment of hRSV infection.

PMID:37371627 | DOI:10.3390/biomedicines11061531

Cancers (Basel). 2023 Jun 15;15(12):3199. doi: 10.3390/cancers15123199.

ABSTRACT

Despite the advancement in drug discovery for cancer therapy, drug repurposing remains an exceptional opportunistic strategy. This approach offers many advantages (faster, safer, and cheaper drugs) typically needed to overcome increased challenges, i.e., side effects, resistance, and costs associated with cancer therapy. However, not all drug classes suit a patient's condition or long-time use. For that, repurposing chronically used medications is more appealing. This review highlights the importance of repurposing anti-diabetic and anti-hypertensive drugs in the global fight against human malignancies. Extensive searches of all available evidence (up to 30 March 2023) on the anti-cancer activities of anti-diabetic and anti-hypertensive agents are obtained from multiple resources (PubMed, Google Scholar, ClinicalTrials.gov, Drug Bank database, ReDo database, and the National Institutes of Health). Interestingly, more than 92 clinical trials are evaluating the anti-cancer activity of 14 anti-diabetic and anti-hypertensive drugs against more than 15 cancer types. Moreover, some of these agents have reached Phase IV evaluations, suggesting promising official release as anti-cancer medications. This comprehensive review provides current updates on different anti-diabetic and anti-hypertensive classes possessing anti-cancer activities with the available evidence about their mechanism(s) and stage of development and evaluation. Hence, it serves researchers and clinicians interested in anti-cancer drug discovery and cancer management.

PMID:37370809 | DOI:10.3390/cancers15123199

Curr Issues Mol Biol. 2023 Jun 11;45(6):5118-5131. doi: 10.3390/cimb45060325.

ABSTRACT

Tezosentan is a vasodilator drug that was originally developed to treat pulmonary arterial hypertension. It acts by inhibiting endothelin (ET) receptors, which are overexpressed in many types of cancer cells. Endothelin-1 (ET1) is a substance produced by the body that causes blood vessels to narrow. Tezosentan has affinity for both ETA and ETB receptors. By blocking the effects of ET1, tezosentan can help to dilate blood vessels, improve the blood flow, and reduce the workload on the heart. Tezosentan has been found to have anticancer properties due to its ability to target the ET receptors, which are involved in promoting cellular processes such as proliferation, survival, neovascularization, immune cell response, and drug resistance. This review intends to demonstrate the potential of this drug in the field of oncology. Drug repurposing can be an excellent way to improve the known profiles of first-line drugs and to solve several resistance problems of these same antineoplastic drugs.

PMID:37367074 | DOI:10.3390/cimb45060325

J Transl Med. 2023 Jun 26;21(1):415. doi: 10.1186/s12967-023-04223-2.

ABSTRACT

BACKGROUND: Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease.

METHODS: We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses.

RESULTS: The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype.

CONCLUSION: We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

PMID:37365631 | DOI:10.1186/s12967-023-04223-2

Int Immunopharmacol. 2023 Jun 24;121:110528. doi: 10.1016/j.intimp.2023.110528. Online ahead of print.

ABSTRACT

Metformin, a biguanide antidiabetic, has been studied for its repurposing effects in oncology. Although a modest effect was observed in a single-agent regimen, metformin can synergize the anti-tumor effects of other modalities. The promising combination for cancer treatment is with immunotherapy. Despite high efficacy for some cancers, immunotherapy could be limited by modulation of the tumor immune microenvironment and the immune exhaustion of cytotoxic immune cells. Combining immunotherapy with metformin, thus, exerted a rescuing effect of immunotherapy and potentiated the anti-tumor effects of each other. Although not fully understood, metformin shows promoting effects of immunotherapy by several mechanisms. Those proposed mechanisms have been partially proven and are suggested for possible therapeutic strategies for cancer treatment. In this review, a state-of-the-art of metformin's boosting effects on immunotherapy is reviewed and discussed. The future directions for metformin research in preclinical and clinical immunotherapy are also suggested.

PMID:37364322 | DOI:10.1016/j.intimp.2023.110528

Multimed Tools Appl. 2023 May 10:1-35. doi: 10.1007/s11042-023-15270-8. Online ahead of print.

ABSTRACT

After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93%, respectively.

PMID:37362739 | PMC:PMC10170456 | DOI:10.1007/s11042-023-15270-8

Russ J Bioorg Chem. 2023 May 11:1-7. doi: 10.1134/S106816202303007X. Online ahead of print.

ABSTRACT

The infection of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started form Wuhan, Chinais a devastating and the incidence rate has increased worldwide. Due to the lack of effective treatment against SARS-CoV-2, various strategies are being tested in China and throughout the world, including drug repurposing. To identify the potent clinical antiretroviral drug candidate against pandemic nCov-19 through computational tools. In this study, we used molecular modelling tool (molecular modelling and molecular dynamics) to identify commercially available drugs that could act on protease proteins of SARS-CoV-2. The result showed that Saquinavir, an antiretroviral medication can be used as a first line agent to treat SARS-CoV-2 infection. Saquinavir showed promising binding to the protease active site compared to other possible antiviral agents such as Nelfinavir and Lopinavir. Structural flexibility is one of the important physical properties that affect protein conformation and function and taking this account we performed molecular dynamics studies. Molecular dynamics studies and free energy calculations suggest that Saquinavir binds better to the COVID-19 protease compared to other known antiretrovirals. Our studies clearly propose repurposing of known protease inhibitors for the treatment of COVID-19 infection. Previously ritonavir and lopinavir were proved an important analogues for SARS and MERS in supressing these viruses. In this study it was found that saquinavir has exhibited good G-score and E-model score compared to other analogues. So saquinavir would be prescribe to cure for nCov-2019 either single drug or maybe in combination with ritonavir.

PMID:37360794 | PMC:PMC10173906 | DOI:10.1134/S106816202303007X

J Microbiol. 2023 Jun 26. doi: 10.1007/s12275-023-00062-4. Online ahead of print.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergence in 2019 led to global health crises and the persistent risk of viral mutations. To combat SARS-CoV-2 variants, researchers have explored new approaches to identifying potential targets for coronaviruses. This study aimed to identify SARS-CoV-2 inhibitors using drug repurposing. In silico studies and network pharmacology were conducted to validate targets and coronavirus-associated diseases to select potential candidates, and in vitro assays were performed to evaluate the antiviral effects of the candidate drugs to elucidate the mechanisms of the viruses at the molecular level and determine the effective antiviral drugs for them. Plaque and cytopathic effect reduction were evaluated, and real-time quantitative reverse transcription was used to evaluate the antiviral activity of the candidate drugs against SARS-CoV-2 variants in vitro. Finally, a comparison was made between the molecular docking binding affinities of fenofibrate and remdesivir (positive control) to conventional and identified targets validated from protein-protein interaction (PPI). Seven candidate drugs were obtained based on the biological targets of the coronavirus, and potential targets were identified by constructing complex disease targets and PPI networks. Among the candidates, fenofibrate exhibited the strongest inhibition effect 1 h after Vero E6 cell infection with SARS-CoV-2 variants. This study identified potential targets for coronavirus disease (COVID-19) and SARS-CoV-2 and suggested fenofibrate as a potential therapy for COVID-19.

PMID:37358709 | DOI:10.1007/s12275-023-00062-4

J Mol Biol. 2023 Jul 15;435(14):168115. doi: 10.1016/j.jmb.2023.168115. Epub 2023 Apr 20.

ABSTRACT

Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed 'anti-Biofilm', a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC50) values extracted from aBiofilm resource. Out of the five MLTs, the Support Vector Machine performed best with Pearson's correlation coefficient of 0.75 on the training/testing data set. The robustness of the developed model was further checked using an independent validation dataset. While analyzing the chemical diversity of the anti-biofilm compounds, we observed that they occupy diverse chemical spaces with parent molecules like furanone, urea, phenolic acids, quinolines, and many more. Use of diverse chemicals as input further signifies the robustness of our predictive models. The three best-performing machine learning models were implemented as a user-friendly 'anti-Biofilm' web server (https://bioinfo.imtech.res.in/manojk/antibiofilm/) with different other modules which make 'anti-Biofilm' a comprehensive platform. Therefore, we hope that our initiative will be helpful for the scientific community engaged in identifying effective anti-biofilm agents to target the problem of antimicrobial resistance.

PMID:37356913 | DOI:10.1016/j.jmb.2023.168115

J Mol Biol. 2023 Jul 15;435(14):168141. doi: 10.1016/j.jmb.2023.168141. Epub 2023 May 4.

ABSTRACT

Ligand binding sites provide essential information for uncovering protein functions and structure-based drug discovery. To facilitate cavity detection and property analysis process, we developed a comprehensive web server, CavityPlus in 2018. CavityPlus applies the CAVITY program to detect potential binding sites in a given protein structure. The CavPharmer, CorrSite, and CovCys tools can then be applied to generate receptor-based pharmacophore models, identify potential allosteric sites, or detect druggable cysteine residues for covalent drug design. While CavityPlus has been widely used, the constantly evolving knowledge and methods make it necessary to improve and extend its functions. This study presents a new version of CavityPlus, CavityPlus 2022 through a series of upgrades. We upgraded the CAVITY tool to greatly speed up cavity detection calculation. We optimized the CavPharmer tool for fast speed and more accurate results. We integrated the newly developed CorrSite2.0 into the CavityPlus 2022 web server for its improved performance of allosteric site prediction. We also added a new CavityMatch module for drug repurposing and protein function studies by searching similar cavities to a given cavity from pre-constructed cavity databases. The new version of CavityPlus is freely available at http://pkumdl.cn:8000/cavityplus/.

PMID:37356903 | DOI:10.1016/j.jmb.2023.168141

J Assoc Physicians India. 2022 Nov;70(11):11-12. doi: 10.5005/japi-11001-0140.

ABSTRACT

A total number of 1,524,161 active cases, 92,941 deaths, and 213 countries have been affected worldwide by COVID-19 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as of 11th April 2020. Much can be attributed to the virus' structural protein, S protein, which determines its host range and tissue tropism and aids its rapid spread. This review aims to summarize numerous researches carried out with respect to the complex and resistant structure of SARS-CoV-2 in addition to the researches performed on various antivirals on the basis of drug repurposing, to aid in better understanding for future researches, clinical trials, and treatment protocols.

PMID:37355950 | DOI:10.5005/japi-11001-0140

Antiviral Res. 2023 Jun 22:105655. doi: 10.1016/j.antiviral.2023.105655. Online ahead of print.

ABSTRACT

The severity of the SARS-CoV-2 pandemic and the recurring (re)emergence of viruses prompted the development of new therapeutic approaches that target viral and host factors crucial for viral infection. Among them, host peptidases cathepsins B and L have been described as essential enzymes during SARS-CoV-2 entry. In this study, we evaluated the effect of potent selective cathepsin inhibitors as antiviral agents. We demonstrated that selective cathepsin B inhibitors, such as the antimicrobial agent nitroxoline and its derivatives, impair SARS-CoV-2 infection in vitro. Antiviral activity observed at early stage of virus entry was cell-type dependent and correlated well with the intracellular content and enzymatic function of cathepsins B or L. Furthermore, tested inhibitors were effective against the ancestral SARS-CoV-2 D614 as well as against the more recent BA.1_4 (Omicron). Taken together, our results highlight the important role of host cysteine cathepsin B in SARS-CoV-2 virus entry and show that cathepsin-specific inhibitors, such as nitroxoline and its derivatives, could be used to treat COVID-19. Finally, these results also suggest that nitroxoline has potential to be further explored as repurposed drug in antiviral therapy.

PMID:37355023 | DOI:10.1016/j.antiviral.2023.105655

Clin Microbiol Infect. 2023 Jun 21:S1198-743X(23)00301-4. doi: 10.1016/j.cmi.2023.06.023. Online ahead of print.

ABSTRACT

OBJECTIVES: The COVID-19 pandemic has revealed a severe need for effective antiviral treatment. The objectives of this study were to assess if preemptive treatment with amantadine for COVID-19 in non-hospitalized persons ≥40 years or adults with comorbidities was able to prevent disease progression and hospitalization. Primary outcomes were clinical status on day 14.

METHODS: Between 9th June 2021 and 27th January 2022, this randomized, double-blinded, placebo-controlled, single-center clinical trial included 242 subjects with a follow-up period of 90 days. Subjects were randomized 1:1 to either amantadine 100 mg or placebo twice daily for five days. The inclusion criteria were confirmed SARS-CoV-2 infection and at least one of (i) age ≥ 40 years, age ≥ 18 years (ii) and at least one comorbidity, or - (iii) and BMI ≥ 30. The study protocol was published at www.

CLINICALTRIALS: gov (unique protocol #02032021) and at www.clinicaltrialregister.eu (EudraCT-number 2021-001177-22).

RESULTS: With 121 participants in each arm, we found no difference in the primary endpoint with 82 participants in the amantadine arm, and 92 participants in the placebo arm with no limitations to activities, respectively, and 25 and 37 with limitations to activities in the amantadine arm and the placebo arm respectively. No participants in either group were admitted to hospital or died. The Odds Ratio of having state severity increased by 1 in the amantadine group versus placebo was 1.8 (Confidence Interval 1.0-3.3, (p=0.051)). At day 7, one participant was hospitalized in each group; throughout the study this increased to five and three participants for amantadine versus placebo treatment (P=0.72). Similarly, at day 7, there was no difference in the status of oropharyngeal swabs. Most participants (108 in each group) were SARS-CoV-2 RNA positive (p=0.84).

CONCLUSIONS: We found no effect of amantadine on disease progression of SARS-CoV-2 infection.

PMID:37353078 | DOI:10.1016/j.cmi.2023.06.023

Cancer Cell. 2023 Jun 19:S1535-6108(23)00208-8. doi: 10.1016/j.ccell.2023.06.001. Online ahead of print.

ABSTRACT

Gastric neuroendocrine carcinomas (G-NEC) are aggressive malignancies with poorly understood biology and a lack of disease models. Here, we use genome sequencing to characterize the genomic landscapes of human G-NEC and its histologic variants. We identify global and subtype-specific alterations and expose hitherto unappreciated gains of MYC family members in a large part of cases. Genetic engineering and lineage tracing in mice delineate a model of G-NEC evolution, which defines MYC as a critical driver and positions the cancer cell of origin to the neuroendocrine compartment. MYC-driven tumors have pronounced metastatic competence and display defined signaling addictions, as revealed by large-scale genetic and pharmacologic screening of cell lines and organoid resources. We create global maps of G-NEC dependencies, highlight critical vulnerabilities, and validate therapeutic targets, including candidates for clinical drug repurposing. Our study gives comprehensive insights into G-NEC biology.

PMID:37352862 | DOI:10.1016/j.ccell.2023.06.001

AMIA Jt Summits Transl Sci Proc. 2023 Jun 16;2023:378-387. eCollection 2023.

ABSTRACT

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, accompanying symptoms, or cotreating AD pathology and its common comorbidities. Our pipeline demonstrated a workable strategy that predicts drug candidates with current knowledge of biological interplays between AD and several vascular diseases.

PMID:37350918 | PMC:PMC10283123

Probiotics Antimicrob Proteins. 2023 Jun 22. doi: 10.1007/s12602-023-10105-2. Online ahead of print.

ABSTRACT

Heart failure (HF) is a global pandemic with increasing prevalence and mortality rates annually. Its main cause is myocardial infarction (MI), followed by rapid cardiac remodeling. Several clinical studies have shown that probiotics can improve the quality of life and reduce cardiovascular risk factors. This systematic review and meta-analysis aimed to investigate the effectiveness of probiotics in preventing HF caused by a MI according to a prospectively registered protocol (PROSPERO: CRD42023388870). Four independent evaluators independently extracted the data using predefined extraction forms and evaluated the eligibility and accuracy of the studies. A total of six studies consisting of 366 participants were included in the systematic review. Probiotics are not significant in intervening left ventricular ejection fraction (LVEF) and high-sensitivity C-reactive protein (hs-CRP) when compared between the intervention group and the control group due to inadequate studies supporting its efficacy. Among sarcopenia indexes, hand grip strength (HGS) showed robust correlations with the Wnt biomarkers (p < 0.05), improved short physical performance battery (SPPB) scores were also strongly correlated with Dickkopf-related protein (Dkk)-3, followed by Dkk-1, and sterol regulatory element-binding protein 1 (SREBP-1) (p < 0.05). The probiotic group showed improvement in total cholesterol (p = 0.01) and uric acid (p = 0.014) compared to the baseline. Finally, probiotic supplements may be an anti-inflammatory, antioxidant, metabolic, and intestinal microbiota modulator in cardiac remodeling conditions. Probiotics have great potential to attenuate cardiac remodeling in HF or post-MI patients while also enhancing the Wnt signaling pathway which can improve sarcopenia under such conditions.

PMID:37349622 | DOI:10.1007/s12602-023-10105-2

Comput Biol Chem. 2023 Jun 16;105:107902. doi: 10.1016/j.compbiolchem.2023.107902. Online ahead of print.

ABSTRACT

Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.

PMID:37348299 | DOI:10.1016/j.compbiolchem.2023.107902

World J Gastroenterol. 2023 May 28;29(20):3103-3118. doi: 10.3748/wjg.v29.i20.3103.

ABSTRACT

BACKGROUND: The transforming growth factor β (TGFβ) signaling pathway plays a crucial role in the development of liver fibrosis by activating TGFβ type II receptor (TGFβR2), followed by the recruitment of TGFβR1 finally triggering downstream signaling pathway.

AIM: To find drugs targeting TGFβR2 that inhibit TGFβR1/TGFβR2 complex formation, theoretically inhibit TGFβ signaling pathway, and thereby ameliorate liver fibrosis.

METHODS: Food and Drug Administration-approved drugs were screened for binding affinity with TGFβR2 by virtual molecular docking. We identified 6 candidates and further explored their potential by Cell Counting Kit-8 (CCK-8) cell cytotoxic experiment to validate toxicity and titrated the best cellular working concentrations. Next, we further demonstrated the detailed molecular working mechanisms using mutagenesis analysis. Finally, we used a mouse model to investigate its potential anti-liver fibrosis effect.

RESULTS: We identified 6 drug candidates. Among these 6 drugs, dihydroergotamine (DHE) shows great ability in reducing fibrotic gene expressions such as collagen, p-SMAD3, and α-SMA in TGFβ induced cellular model of liver fibrosis in LX-2 cells. Furthermore, we demonstrated that DHE binds to TGFβR2. Moreover, mutation of Leu27, Phe30, Thr51, Ser52, Ile53, and Glu55 of TGFβR2 disrupted the binding of TGFβR2 with DHE. In addition, DHE significantly improved liver fibrosis, as evidenced by Masson's trichrome staining of liver sections. This is further supported by the width and the velocity of the portal vein, and serum markers of liver function. In line with those observations, DHE also decreased macrophages infiltration and extracellular matrix deposition in the liver.

CONCLUSION: DHE alleviates liver fibrosis by binding to TGFβR2 thereby suppressing TGFβ signaling pathway. We show here that as far as drug repurposing, DHE has great potential to treat liver fibrosis.

PMID:37346154 | PMC:PMC10280794 | DOI:10.3748/wjg.v29.i20.3103

Protein Cell. 2023 Jun 22:pwad038. doi: 10.1093/procel/pwad038. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) is a highly heterogeneous cancer and exploring novel therapeutic options is a pressing issue that needs to be addressed. Here, we established human CRC tumor-derived organoids that well represent both morphological and molecular heterogeneities of original tumors. To efficiently identify repurposed drugs for CRC, we developed a robust organoid-based drug screening system. By combining the repurposed drug library and computation-based drug prediction, 335 drugs were tested and 34 drugs with anti-CRC effects were identified. More importantly, we conducted a detailed transcriptome analysis of drug responses and divided the drug response signatures into five representative patterns: differentiation induction, growth inhibition, metabolism inhibition, immune response promotion and cell cycle inhibition. The anticancer activities of drug candidates were further validated in the established patient-derived organoids-based xenograft (PDOX) system in vivo. We found that fedratinib, trametinib and bortezomib exhibited effective anticancer effects. Furthermore, the concordance and discordance of drug response signatures between organoids in vitro and pairwise PDOX in vivo were evaluated. Our study offers an innovative approach for drug discovery, and the representative transcriptome features of drug responses provide valuable resources for developing novel clinical treatments for CRC.

PMID:37345888 | DOI:10.1093/procel/pwad038

J Transl Med. 2023 Jun 21;21(1):403. doi: 10.1186/s12967-023-04263-8.

ABSTRACT

Metformin is a well-known anti-diabetic drug that has been repurposed for several emerging applications, including as an anti-cancer agent. It boasts the distinct advantages of an excellent safety and tolerability profile and high cost-effectiveness at less than one US dollar per daily dose. Epidemiological evidence reveals that metformin reduces the risk of cancer and decreases cancer-related mortality in patients with diabetes; however, the exact mechanisms are not well understood. Energy metabolism may be central to the mechanism of action. Based on altering whole-body energy metabolism or cellular state, metformin's modes of action can be divided into two broad, non-mutually exclusive categories: "direct effects", which induce a direct effect on cancer cells, independent of blood glucose and insulin levels, and "indirect effects" that arise from systemic metabolic changes depending on blood glucose and insulin levels. In this review, we summarize an updated account of the current knowledge on metformin antitumor action, elaborate on the underlying mechanisms in terms of the hallmarks of cancer, and propose potential applications for repurposing metformin for cancer therapeutics.

PMID:37344841 | PMC:PMC10286395 | DOI:10.1186/s12967-023-04263-8