Naunyn Schmiedebergs Arch Pharmacol. 2023 Oct 14. doi: 10.1007/s00210-023-02722-z. Online ahead of print.

ABSTRACT

Colon cancer is one of the most common cancers and one of the main causes of death worldwide. Therefore, new treatment methods with better efficiency and fewer risks are very necessary. Mebendazole (MBZ), a drug commonly used for helminthic infections, has recently received attention as a suitable candidate for the treatment of various cancers. This study aimed to investigate, in vitro and in vivo, anticancer activity and selectivity Index of MBZ on colon cancer. HT-29 (human colorectal adenocarcinoma) and MCF-10 (non-tumorigenic epithelial) cell lines were treated with MBZ and Doxorubicin (DOX; positive control drug). IC50 values were estimated using methyl thiazole diphenyl-tetrazolium bromide (MTT) assay. We employed flow cytometry using annexin V-FITC and propidium iodide dyes. For the animal study, colon cancer was subcutaneously induced by CT26 cells (mouse colon cancer) in Bulb/C mice. The mice were treated with 0.05 of LD50, intraperitoneal, every other day for 35 days. Finally, the survival rate, tumor volume, and tumor weight were calculated. Our results demonstrated that IC50 values after 72 h for HT29 and MCF-10 cell lines were 0.29 ± 0.04 µM and 0.80 ± 0.02 µM, respectively. MBZ was more selective than DOX in inhibiting the proliferation of cancer cells compared to normal cells (2. 75 vs. 2.45). Annexin V/PI staining demonstrated that MBZ treatment at IC50 concentrations induced (78 ± 12%) apoptosis in the HT29 cancer cell line after 48 h (P ≤ 0.0001). Also, in mice bearing colon cancer, MBZ significantly reduced the tumor volume (1177 ± 1109 mm3; P ≤ 0.001) and tumor weight (2.30 ± 1.97 g; P ≤ 0.0001) compared to the negative control group (weight 12.45 ± 2.0 g; volume 7346 ± 1077). Also, MBZ increases mean survival time (MST) and increase life span (ILS) percentage in the animal study (51.2 ± 37% vs 93%, respectively). This study suggests that mebendazole strongly and selectively inhibits proliferation and induces apoptosis in colon cancer cells. It may be, accordingly, a promising drug for clinical research and application.

PMID:37837472 | DOI:10.1007/s00210-023-02722-z

Alzheimers Dement. 2023 Oct 14. doi: 10.1002/alz.13463. Online ahead of print.

ABSTRACT

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.

METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.

RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.

DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.

HIGHLIGHTS: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

PMID:37837420 | DOI:10.1002/alz.13463

J Biomol Struct Dyn. 2023 Oct 14:1-11. doi: 10.1080/07391102.2023.2268176. Online ahead of print.

ABSTRACT

Breast cancer (BC) accounts for 30% of all diagnosed cases of cancer in women and remains a leading cause of cancer-related deaths among women worldwide. The current study looks for a protein from the anti-apoptotic/pro-survival BCL-2 family whose overexpression reduces survivability in BC patients and a potential inhibitor for the protein. We found BCL-2A1/BFL1 protein with high expression linked to low survivability in BC. The protein shows prognosis in 8 out of 29 categories, whereas no other family member manifests this property. Out of 7379 compounds, three small molecules (CHEMBL9509, CHEMBL2104550 and CHEMBL3545011) form an H-bond with BCL-2A1/BFL1 protein's unique residue Cys55. Of the three small molecules, we found CHEMBL9509 (Silibinin) to be a potent inhibitor. The compound forms a stable H-bond with the residue Cys55 with the lowest binding energy compared to the other two compounds. It remains stable in the BH3 binding region for more than 100 ns, whereas the other two detach from the region. Additionally, the compound is found to be better than Venetoclax and Nematoclax. We firmly believe in the compound CHEMBL9509 potency to halt BC's progression by inhibiting the BCL-2A1/BFL1 protein, increasing patients' survivability.Communicated by Ramaswamy H. Sarma.

PMID:37837418 | DOI:10.1080/07391102.2023.2268176

Pharmacol Res. 2023 Oct 11:106960. doi: 10.1016/j.phrs.2023.106960. Online ahead of print.

ABSTRACT

Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.

PMID:37832859 | DOI:10.1016/j.phrs.2023.106960

Nucleic Acids Res. 2023 Oct 13:gkad832. doi: 10.1093/nar/gkad832. Online ahead of print.

ABSTRACT

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

PMID:37831083 | DOI:10.1093/nar/gkad832

Aust N Z J Psychiatry. 2023 Oct 13:48674231203898. doi: 10.1177/00048674231203898. Online ahead of print.

ABSTRACT

In this paper, the case study of ketamine as a new treatment for severe depression is used to outline the challenges of repurposing established medicines and we suggest potential solutions. The antidepressant effects of generic racemic ketamine were identified over 20 years ago, but there were insufficient incentives for commercial entities to pursue its registration, or support for non-commercial entities to fill this gap. As a result, the evaluation of generic ketamine was delayed, piecemeal, uncoordinated, and insufficient to gain approval. Meanwhile, substantial commercial investment enabled the widespread registration of a patented, intranasal s-enantiomeric ketamine formulation (Spravato®) for depression. However, Spravato is priced at $600-$900/dose compared to ~$5/dose for generic ketamine, and the ~AUD$100 million annual government investment requested in Australia (to cover drug costs alone) has been rejected twice, leaving this treatment largely inaccessible for Australian patients 2 years after Therapeutic Goods Administration approval. Moreover, emerging evidence indicates that generic racemic ketamine is at least as effective as Spravato, but no comparative trials were required for regulatory approval and have not been conducted. Without action, this story will repeat regularly in the next decade with a new wave of psychedelic-assisted psychotherapy treatments, for which the original off-patent molecules could be available at low-cost and reduce the overall cost of treatment. Several systemic reforms are required to ensure that affordable, effective options become accessible; these include commercial incentives, public and public-private funding schemes, reduced regulatory barriers and more coordinated international public funding schemes to support translational research.

PMID:37830221 | DOI:10.1177/00048674231203898

Chem Sci. 2023 Aug 8;14(39):10684-10701. doi: 10.1039/d3sc02139d. eCollection 2023 Oct 11.

ABSTRACT

Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively. However, there are still two challenges in applying AIDD to guide TCM drug discovery: the lack of a large amount of standardized TCM-related information and AIDD is prone to pathological failures in out-of-domain data. We have released TCM Database@Taiwan in 2011, and it has been widely disseminated and used. Now, we developed TCMBank, the largest systematic free TCM database, which is an extension of TCM Database@Taiwan. TCMBank contains 9192 herbs, 61 966 ingredients (unduplicated), 15 179 targets, 32 529 diseases, and their pairwise relationships. By integrating multiple data sources, TCMBank provides 3D structure information of ingredients and provides a standard list and detailed information on herbs, ingredients, targets and diseases. TCMBank has an intelligent document identification module that continuously adds TCM-related information retrieved from the literature in PubChem. In addition, driven by TCMBank big data, we developed an ensemble learning-based drug discovery protocol for identifying potential leads and drug repurposing. We take colorectal cancer and Alzheimer's disease as examples to demonstrate how to accelerate drug discovery by artificial intelligence. Using TCMBank, researchers can view literature-driven relationship mapping between herbs/ingredients and genes/diseases, allowing the understanding of molecular action mechanisms for ingredients and identification of new potentially effective treatments. TCMBank is available at https://TCMBank.CN/.

PMID:37829020 | PMC:PMC10566508 | DOI:10.1039/d3sc02139d

Annu Rev Med. 2023 Oct 12. doi: 10.1146/annurev-med-060622-101239. Online ahead of print.

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD heterogeneity has hampered progress in developing pharmacotherapies that affect disease progression. This issue can be addressed by precision medicine approaches, which focus on understanding an individual's disease risk, and tailoring management based on pathobiology, environmental exposures, and psychosocial issues. There is an urgent need to identify COPD patients at high risk for poor outcomes and to understand at a mechanistic level why certain individuals are at high risk. Genetics, omics, and network analytic techniques have started to dissect COPD heterogeneity and identify patients with specific pathobiology. Drug repurposing approaches based on biomarkers of specific inflammatory processes (i.e., type 2 inflammation) are promising. As larger data sets, additional omics, and new analytical approaches become available, there will be enormous opportunities to identify high-risk individuals and treat COPD patients based on their specific pathophysiological derangements. These approaches show great promise for risk stratification, early intervention, drug repurposing, and developing novel therapeutic approaches for COPD. Expected final online publication date for the Annual Review of Medicine, Volume 75 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

PMID:37827193 | DOI:10.1146/annurev-med-060622-101239

Nat Commun. 2023 Oct 12;14(1):6156. doi: 10.1038/s41467-023-41876-5.

ABSTRACT

Raynaud's phenomenon (RP) is a common vasospastic disorder that causes severe pain and ulcers, but despite its high reported heritability, no causal genes have been robustly identified. We conducted a genome-wide association study including 5,147 RP cases and 439,294 controls, based on diagnoses from electronic health records, and identified three unreported genomic regions associated with the risk of RP (p < 5 × 10-8). We prioritized ADRA2A (rs7090046, odds ratio (OR) per allele: 1.26; 95%-CI: 1.20-1.31; p < 9.6 × 10-27) and IRX1 (rs12653958, OR: 1.17; 95%-CI: 1.12-1.22, p < 4.8 × 10-13) as candidate causal genes through integration of gene expression in disease relevant tissues. We further identified a likely causal detrimental effect of low fasting glucose levels on RP risk (rG = -0.21; p-value = 2.3 × 10-3), and systematically highlighted drug repurposing opportunities, like the antidepressant mirtazapine. Our results provide the first robust evidence for a strong genetic contribution to RP and highlight a so far underrated role of α2A-adrenoreceptor signalling, encoded at ADRA2A, as a possible mechanism for hypersensitivity to catecholamine-induced vasospasms.

PMID:37828025 | DOI:10.1038/s41467-023-41876-5

JCO Precis Oncol. 2023 Sep;7:e2300261. doi: 10.1200/PO.23.00261.

ABSTRACT

Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.

PMID:37824797 | DOI:10.1200/PO.23.00261

Hum Mol Genet. 2023 Oct 12:ddad174. doi: 10.1093/hmg/ddad174. Online ahead of print.

ABSTRACT

Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N = 1 296 908) for IS together with molecular QTLs data, including mRNA, splicing, enhancer RNA (eRNA), and protein expression data from up to 50 tissues (total N = 11 588). We identify 136 genes/eRNA/proteins associated with IS risk across 60 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.

PMID:37824084 | DOI:10.1093/hmg/ddad174

Front Immunol. 2023 Sep 26;14:1254873. doi: 10.3389/fimmu.2023.1254873. eCollection 2023.

ABSTRACT

INTRODUCTION: Severe COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features, suggesting that severe COVID-19 is a form of viral sepsis. Our objective was to identify shared gene expression trajectories strongly associated with eventual mortality between severe COVID-19 patients and contemporaneous non-COVID-19 sepsis patients in the intensive care unit (ICU) for potential therapeutic implications.

METHODS: Whole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways. Using systems biology methods, drug candidates targeting key genes in the pathophysiology of COVID-19 and sepsis were identified.

RESULTS: When compared to survivors, non-survivors (irrespective of COVID-19 status) had 3.6-fold more "persistent" genes (genes that stayed up/downregulated at both timepoints) (4,289 vs. 1,186 genes); these included persistently downregulated genes in T-cell signaling and persistently upregulated genes in select innate immune and metabolic pathways, indicating unresolved immune dysfunction in non-survivors, while resolution of these processes occurred in survivors. These findings of persistence were further confirmed using two publicly available datasets of COVID-19 and sepsis patients. Systems biology methods identified multiple immunomodulatory drug candidates that could target this persistent immune dysfunction, which could be repurposed for possible therapeutic use in both COVID-19 and sepsis.

DISCUSSION: Transcriptional evidence of persistent immune dysfunction was associated with 28-day mortality in both COVID-19 and non-COVID-19 septic patients. These findings highlight the opportunity for mitigating common mechanisms of immune dysfunction with immunomodulatory therapies for both diseases.

PMID:37822940 | PMC:PMC10562687 | DOI:10.3389/fimmu.2023.1254873

J Exp Clin Cancer Res. 2023 Oct 11;42(1):264. doi: 10.1186/s13046-023-02849-0.

ABSTRACT

BACKGROUND: Disseminated tumor cells (DTCs) can enter a dormant state and cause no symptoms in cancer patients. On the other hand, the dormant DTCs can reactivate and cause metastases progression and lethal relapses. In prostate cancer (PCa), relapse can happen after curative treatments such as primary tumor removal. The impact of surgical removal on PCa dissemination and dormancy remains elusive. Furthermore, as dormant DTCs are asymptomatic, dormancy-induction can be an operational cure for preventing metastases and relapse of PCa patients.

METHODS: We used a PCa subcutaneous xenograft model and species-specific PCR to survey the DTCs in various organs at different time points of tumor growth and in response to tumor removal. We developed in vitro 2D and 3D co-culture models to recapitulate the dormant DTCs in the bone microenvironment. Proliferation assays, fluorescent cell cycle reporter, qRT-PCR, and Western Blot were used to characterize the dormancy phenotype. We performed RNA sequencing to determine the dormancy signature of PCa. A drug repurposing algorithm was applied to predict dormancy-inducing drugs and a top candidate was validated for the efficacy and the mechanism of dormancy induction.

RESULTS: We found DTCs in almost all mouse organs examined, including bones, at week 2 post-tumor cell injections. Surgical removal of the primary tumor reduced the overall DTC abundance, but the DTCs were enriched only in the bones. We found that osteoblasts, but not other cells of the bones, induced PCa cell dormancy. RNA-Seq revealed the suppression of mitochondrial-related biological processes in osteoblast-induced dormant PCa cells. Importantly, the mitochondrial-related biological processes were found up-regulated in both circulating tumor cells and bone metastases from PCa patients' data. We predicted and validated the dormancy-mimicking effect of PF-562,271 (PF-271), an inhibitor of focal adhesion kinase (FAK) in vitro. Decreased FAK phosphorylation and increased nuclear translocation were found in both co-cultured and PF-271-treated C4-2B cells, suggesting that FAK plays a key role in osteoblast-induced PCa dormancy.

CONCLUSIONS: Our study provides the first insights into how primary tumor removal enriches PCa cell dissemination in the bones, defines a unique osteoblast-induced PCa dormancy signature, and identifies FAK as a PCa cell dormancy gatekeeper.

PMID:37821954 | DOI:10.1186/s13046-023-02849-0

Sci Transl Med. 2023 Oct 11;15(717):eade1844. doi: 10.1126/scitranslmed.ade1844. Epub 2023 Oct 11.

ABSTRACT

Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer. Although cSCC contributes to substantial morbidity and mortality in high-risk individuals, deployment of otherwise effective chemoprevention of cSCC is limited by toxicities. Our systematic computational drug repurposing screen predicted that selumetinib, a MAPK (mitogen-activated protein kinase) kinase inhibitor (MEKi), would reverse transcriptional signatures associated with cSCC development, consistent with our genomic analysis implicating MEK as a chemoprevention target. Although systemic MEKi suppresses the formation of cSCC in mice, systemic MEKi can cause severe adverse effects. Here, we report the development of a metabolically labile MEKi, NFX-179, designed to potently and selectively suppress the MAPK pathway in the skin before rapid metabolism in the systemic circulation. NFX-179 was identified on the basis of its biochemical and cellular potency, selectivity, and rapid metabolism upon systemic absorption. In our ultraviolet-induced cSCC mouse model, topical application of NFX-179 gel reduced the formation of new cSCCs by an average of 60% at doses of 0.1% and greater at 28 days. We further confirmed the localized nature of these effects in an additional split-mouse randomized controlled study where suppression of cSCC was observed only in drug-treated areas. No toxicities were observed. NFX-179 inhibits the growth of human SCC cell lines in a dose-dependent manner, and topical NFX-179 application penetrates human skin and inhibits MAPK signaling in human cSCC explants. Together, our data provide a compelling rationale for using topical MEK inhibition through the application of NFX-179 gel as an effective strategy for cSCC chemoprevention.

PMID:37820007 | DOI:10.1126/scitranslmed.ade1844

J Control Release. 2023 Oct 8:S0168-3659(23)00664-8. doi: 10.1016/j.jconrel.2023.10.014. Online ahead of print.

ABSTRACT

The widespread use of small interfering RNA (siRNA) is limited by the multiple extra- and intracellular barriers upon in vivo administration. Hence, suitable delivery systems, based on siRNA encapsulation in nanoparticles or its conjugation to targeting ligands, have been developed. Nevertheless, at the intracellular level, these state-of-the-art delivery systems still suffer from a low endosomal escape efficiency. Consequently, the bulk of the endocytosed siRNA drug rapidly accumulates in the lysosomal compartment. We recently reported that a wide variety of cationic amphiphilic drugs (CADs) can promote small nucleic acid delivery from the endolysosomal compartment into the cytosol via transient induction of lysosomal membrane permeabilization. Here, we describe the identification of alternate siRNA delivery enhancers from the NIH Clinical Compound Collection that do not have the typical physicochemical properties of CADs. Additionally, we demonstrate improved endolysosomal escape of siRNA via a cholesterol conjugate and polymeric carriers with the α1-adrenergic antagonist prazosin, which was identified as the best performing delivery enhancer from the compound screen. A more detailed assessment of the mode-of-action of prazosin suggests that a different cellular phenotype compared to typical CAD adjuvants drives cytosolic siRNA delivery. As it has been described in the literature that prazosin also induces cancer cell apoptosis and promotes antigen cross-presentation in dendritic cells, the proof-of-concept data in this work provides opportunities for the repurposing of prazosin in an anti-cancer combination strategy with siRNA.

PMID:37816483 | DOI:10.1016/j.jconrel.2023.10.014

Int J Biol Macromol. 2023 Oct 8:127208. doi: 10.1016/j.ijbiomac.2023.127208. Online ahead of print.

ABSTRACT

With the advancements of high throughput computational screening procedures, drug repurposing became the privileged framework for drug discovery. The structure-based drug discovery is the widely used method of drug repurposing, consisting of computational screening of compounds and testing them in-vitro. This current method of repurposing leaves room for mechanistic insights into how these screened hits interact with and influence their targets. We addressed this gap in the current study by integrating highly sensitive biophysical methods into existing computational repurposing methods. We also corroborated our computational and biophysical findings on H37Rv for the anti-mycobacterial action of selected drugs in-vitro and ex-vivo conditions. Atosiban and Rutin were screened as highly interacting hits against HemD through multi-stage docking and were cross-validated in biophysical studies. The affinity of these drugs (K ~ 106 M-1) was quantified using fluorescence quenching studies. Differential Scanning Fluorimetry (DSF) and urea-based chemical denaturation studies revealed a destabilizing effect of these drugs on target which was further validated using MD simulations. Conformational rearrangements of secondary structures were established using CD spectra and intrinsic fluorescence. Furthermore, Atosiban and Rutin inhibited M.tb growth in-vitro and ex-vivo while remaining non-toxic to mice peritoneal macrophages.

PMID:37816464 | DOI:10.1016/j.ijbiomac.2023.127208

NPJ Syst Biol Appl. 2023 Oct 10;9(1):49. doi: 10.1038/s41540-023-00311-7.

ABSTRACT

Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .

PMID:37816770 | DOI:10.1038/s41540-023-00311-7

Nat Commun. 2023 Oct 10;14(1):6332. doi: 10.1038/s41467-023-41828-z.

ABSTRACT

Drug combinations are key to circumvent resistance mechanisms compromising response to single anti-cancer targeted therapies. The implementation of combinatorial approaches involving MEK1/2 or KRASG12C inhibitors in the context of KRAS-mutated lung cancers focuses fundamentally on targeting KRAS proximal activators or effectors. However, the antitumor effect is highly determined by compensatory mechanisms arising in defined cell types or tumor subgroups. A potential strategy to find drug combinations targeting a larger fraction of KRAS-mutated lung cancers may capitalize on the common, distal gene expression output elicited by oncogenic KRAS. By integrating a signature-driven drug repurposing approach with a pairwise pharmacological screen, here we show synergistic drug combinations consisting of multi-tyrosine kinase PKC inhibitors together with MEK1/2 or KRASG12C inhibitors. Such combinations elicit a cytotoxic response in both in vitro and in vivo models, which in part involves inhibition of the PKC inhibitor target AURKB. Proteome profiling links dysregulation of MYC expression to the effect of both PKC inhibitor-based drug combinations. Furthermore, MYC overexpression appears as a resistance mechanism to MEK1/2 and KRASG12C inhibitors. Our study provides a rational framework for selecting drugs entering combinatorial strategies and unveils MEK1/2- and KRASG12C-based therapies for lung cancer.

PMID:37816716 | DOI:10.1038/s41467-023-41828-z

Sci Rep. 2023 Oct 9;13(1):17037. doi: 10.1038/s41598-023-43911-3.

ABSTRACT

Drug repurposing is an important strategy in COVID-19 treatment, but many clinically approved compounds have not been extensively studied in the context of embryogenesis, thus limiting their administration during pregnancy. Here we used the zebrafish embryo model organism to test the effects of 162 marketed drugs on cardiovascular development. Among the compounds used in the clinic for COVD-19 treatment, we found that Remdesivir led to reduced body size and heart functionality at clinically relevant doses. Ritonavir and Baricitinib showed reduced heart functionality and Molnupiravir and Baricitinib showed effects on embryo activity. Sabizabulin was highly toxic at concentrations only 5 times higher than Cmax and led to a mean mortality of 20% at Cmax. Furthermore, we tested if zebrafish could be used as a model to study inflammatory response in response to spike protein treatment and found that Remdesivir, Ritonavir, Molnupiravir, Baricitinib as well as Sabizabulin counteracted the inflammatory response related gene expression upon SARS-CoV-2 spike protein treatment. Our results show that the zebrafish allows to study immune-modulating properties of COVID-19 compounds and highlights the need to rule out secondary defects of compound treatment on embryogenesis. All results are available on a user friendly web-interface https://share.streamlit.io/alernst/covasc_dataapp/main/CoVasc_DataApp.py that provides a comprehensive overview of all observed phenotypic effects and allows personalized search on specific compounds or group of compounds. Furthermore, the presented platform can be expanded for rapid detection of developmental side effects of new compounds for treatment of COVID-19 and further viral infectious diseases.

PMID:37813860 | DOI:10.1038/s41598-023-43911-3

Genomics Inform. 2023 Sep;21(3):e31. doi: 10.5808/gi.23011. Epub 2023 Sep 27.

ABSTRACT

Multiple myeloma (MM) is a hematological malignancy. It is widely believed that genetic factors play a significant role in the development of MM, as investigated in numerous studies. However, the application of genomic information for clinical purposes, including diagnostic and prognostic biomarkers, remains largely confined to research. In this study, we utilized genetic information from the Genomic-Driven Clinical Implementation for Multiple Myeloma database, which is dedicated to clinical trial studies on MM. This genetic information was sourced from the genome-wide association studies catalog database. We prioritized genes with the potential to cause MM based on established annotations, as well as biological risk genes for MM, as potential drug target candidates. The DrugBank database was employed to identify drug candidates targeting these genes. Our research led to the discovery of 14 MM biological risk genes and the identification of 10 drugs that target three of these genes. Notably, only one of these 10 drugs, panobinostat, has been approved for use in MM. The two most promising genes, calcium signal-modulating cyclophilin ligand (CAMLG) and histone deacetylase 2 (HDAC2), were targeted by four drugs (cyclosporine, belinostat, vorinostat, and romidepsin), all of which have clinical evidence supporting their use in the treatment of MM. Interestingly, five of the 10 drugs have been approved for other indications than MM, but they may also be effective in treating MM. Therefore, this study aimed to clarify the genomic variants involved in the pathogenesis of MM and highlight the potential benefits of these genomic variants in drug discovery.

PMID:37813627 | DOI:10.5808/gi.23011