Bioinformatics. 2023 Jun 28:btad411. doi: 10.1093/bioinformatics/btad411. Online ahead of print.

ABSTRACT

MOTIVATION: Screening new drug-target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework.

RESULTS: We developed MDTips, a Multimodal-data based Drug-Target interaction prediction system, by fusing the knowledge graphs, gene expression profiles, and structural information of drugs/targets. MDTips yielded accurate and robust performance on DTI predictions. We found that multimodal fusion learning can fully consider the importance of each modality and incorporate information from multiple aspects, thus improving model performance. Extensive experimental results demonstrate that deep learning-based encoders (i.e., Attentive FP and Transformer) outperform traditional chemical descriptors/fingerprints, and MDTips outperforms other state-of-the-art prediction models. MDTips is designed to predict the input drugs' candidate targets, side effects, and indications with all available modalities. Via MDTips, we reverse-screened candidate targets of 6,766 drugs, which can be used for drug repurposing and discovery.

AVAILABILITY: https://github.com/XiaoqiongXia/MDTips and https://doi.org/10.5281/zenodo.7560544.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37379157 | DOI:10.1093/bioinformatics/btad411

Pharmaceutics. 2023 Jun 7;15(6):1673. doi: 10.3390/pharmaceutics15061673.

ABSTRACT

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

PMID:37376121 | DOI:10.3390/pharmaceutics15061673

Pharmaceutics. 2023 May 26;15(6):1600. doi: 10.3390/pharmaceutics15061600.

ABSTRACT

Cancer therapeutics have undergone immense research over the past decade. While chemotherapies remain the mainstay treatments for many cancers, the advent of new molecular techniques has opened doors for more targeted modalities towards cancer cells. Although immune checkpoint inhibitors (ICIs) have demonstrated therapeutic efficacy in treating cancer, adverse side effects related to excessive inflammation are often reported. There is a lack of clinically relevant animal models to probe the human immune response towards ICI-based interventions. Humanized mouse models have emerged as valuable tools for pre-clinical research to evaluate the efficacy and safety of immunotherapy. This review focuses on the establishment of humanized mouse models, highlighting the challenges and recent advances in these models for targeted drug discovery and the validation of therapeutic strategies in cancer treatment. Furthermore, the potential of these models in the process of uncovering novel disease mechanisms is discussed.

PMID:37376049 | DOI:10.3390/pharmaceutics15061600

Pharmaceuticals (Basel). 2023 Jun 1;16(6):826. doi: 10.3390/ph16060826.

ABSTRACT

Chagas disease (ChD), caused by Trypanosoma cruzi, is the most serious parasitosis in the western hemisphere. Benznidazole and nifurtimox, the only two trypanocidal drugs, are expensive, difficult to obtain, and have severe side effects. Nitazoxanide has shown to be effective against protozoa, bacteria, and viruses. This study aimed to evaluate the nitazoxanide efficacy against the Mexican T. cruzi Ninoa strain in mice. Infected animals were orally treated for 30 days with nitazoxanide (100 mg/kg) or benznidazole (10 mg/kg). The clinical, immunological, and histopathological conditions of the mice were evaluated. Nitazoxanide- or benznidazole-treated mice had longer survival and less parasitemia than those without treatment. Antibody production in the nitazoxanide-treated mice was of the IgG1-type and not of the IgG2-type as in the benznidazole-treated mice. Nitazoxanide-treated mice had significantly high IFN-γ levels compared to the other infected groups. Serious histological damage could be prevented with nitazoxanide treatment compared to without treatment. In conclusion, nitazoxanide decreased parasitemia levels, indirectly induced the production of IgG antibodies, and partially prevented histopathological damage; however, it did not show therapeutic superiority compared to benznidazole in any of the evaluated aspects. Therefore, the repositioning of nitazoxanide as an alternative treatment against ChD could be considered, since it did not trigger adverse effects that worsened the pathological condition of the infected mice.

PMID:37375773 | DOI:10.3390/ph16060826

Nutrients. 2023 Jun 18;15(12):2790. doi: 10.3390/nu15122790.

ABSTRACT

Melanoma is the most invasive and lethal skin cancer. Recently, PD-1/PD-L1 pathway modulation has been applied to cancer therapy due to its remarkable clinical efficacy. SH003, a mixture of natural products derived from Astragalus membranaceus, Angelica gigas, and Trichosanthes kirilowii, and formononetin (FMN), an active constituent of SH003, exhibit anti-cancer and anti-oxidant properties. However, few studies have reported on the anti-melanoma activities of SH003 and FMN. This work aimed to elucidate the anti-melanoma effects of SH003 and FMN through the PD-1/PD-L1 pathway, using B16F10 cells and CTLL-2 cells. Results showed that SH003 and FMN reduced melanin content and tyrosinase activity induced by α-MSH. Moreover, SH003 and FMN suppressed B16F10 growth and arrested cells at the G2/M phase. SH003 and FMN also led to cell apoptosis with increases in PARP and caspase-3 activation. The pro-apoptotic effects were further enhanced when combined with cisplatin. In addition, SH003 and FMN reversed the increased PD-L1 and STAT1 phosphorylation levels induced by cisplatin in the presence of IFN-γ. SH003 and FMN also enhanced the cytotoxicity of CTLL-2 cells against B16F10 cells. Therefore, the mixture of natural products SH003 demonstrates therapeutic potential in cancer treatment by exerting anti-melanoma effects through the PD-1/PD-L1 pathway.

PMID:37375695 | DOI:10.3390/nu15122790

Int J Mol Sci. 2023 Jun 12;24(12):10033. doi: 10.3390/ijms241210033.

ABSTRACT

Psoriasis is a chronic skin disease that affects millions of people worldwide. In 2014, psoriasis was recognized by the World Health Organization (WHO) as a serious non-communicable disease. In this study, a systems biology approach was used to investigate the underlying pathogenic mechanism of psoriasis and identify the potential drug targets for therapeutic treatment. The study involved the construction of a candidate genome-wide genetic and epigenetic network (GWGEN) through big data mining, followed by the identification of real GWGENs of psoriatic and non-psoriatic using system identification and system order detection methods. Core GWGENs were extracted from real GWGENs using the Principal Network Projection (PNP) method, and the corresponding core signaling pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Comparing core signaling pathways of psoriasis and non-psoriasis and their downstream cellular dysfunctions, STAT3, CEBPB, NF-κB, and FOXO1 are identified as significant biomarkers of pathogenic mechanism and considered as drug targets for the therapeutic treatment of psoriasis. Then, a deep neural network (DNN)-based drug-target interaction (DTI) model was trained by the DTI dataset to predict candidate molecular drugs. By considering adequate regulatory ability, toxicity, and sensitivity as drug design specifications, Naringin, Butein, and Betulinic acid were selected from the candidate molecular drugs and combined into potential multi-molecule drugs for the treatment of psoriasis.

PMID:37373186 | DOI:10.3390/ijms241210033

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