medRxiv [Preprint]. 2024 Nov 27:2024.11.25.24317794. doi: 10.1101/2024.11.25.24317794.

ABSTRACT

RATIONALE: Accelerated decline in lung function contributes to the development of chronic respiratory disease. Despite evidence for a genetic component, few genetic associations with lung function decline have been identified.

OBJECTIVES: To evaluate genome-wide associations and putative downstream functionality of genetic variants with lung function decline in diverse general population cohorts.

METHODS: We conducted genome-wide association study (GWAS) analyses of decline in the forced expiratory volume in the first second (FEV 1 ), forced vital capacity (FVC), and their ratio (FEV 1 /FVC) in participants across six cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. Genotypes were imputed to TOPMed (CHARGE cohorts) or Haplotype Reference Consortium (HRC) (UK Biobank) reference panels, and GWAS analyses used generalized estimating equation models with robust standard error. Models were stratified by cohort, ancestry, and sex, and adjusted for important lung function confounders and genotype principal components. Results were combined in cross-ancestry and ancestry-specific meta-analyses. Selected top variants were tested for replication in two independent COPD-enriched cohorts.

MEASUREMENTS AND MAIN RESULTS: Our discovery analyses included 52,056 self-reported White (N=44,988), Black (N=5,788), Hispanic (N=550), and Chinese American (N=730) participants with a mean of 2.3 spirometry measurements and 8.6 years of follow-up. Functional mapping of GWAS meta-analysis results identified 361 distinct genome-wide significant (p<5E-08) variants in one or more of the FEV 1 , FVC, and FEV 1 /FVC decline phenotypes, which overlapped with previously reported genetic signals for several related pulmonary traits. Of these, 8 variants, or 20.5% of the variant set available for replication testing, were nominally associated (p<0.05) with at least one decline phenotype in COPD-enriched cohorts (White [N=4,778] and Black [N=1,118]). Using the GWAS results, gene-level analysis implicated 38 genes, including eight ( XIRP2 , GRIN2D , SATB1 , MARCHF4 , SIPA1L2 , ANO5 , H2BC10 , and FAF2 ) with consistent associations across ancestries or decline phenotypes. Annotation class analysis revealed significant enrichment of several regulatory processes for corticosteroid biosynthesis and metabolism. Drug repurposing analysis identified 43 approved compounds targeting eight of the implicated 38 genes.

CONCLUSIONS: Our multi-ancestry GWAS meta-analyses identified numerous genetic loci associated with lung function decline. These findings contribute knowledge to the genetic architecture of lung function decline, provide evidence for a role of endogenous corticosteroids in the etiology of lung function decline, and identify drug targets that merit further study for potential repurposing to slow lung function decline and treat lung disease.

PMID:39649580 | PMC:PMC11623738 | DOI:10.1101/2024.11.25.24317794

J Comput Biol. 2024 Dec 9. doi: 10.1089/cmb.2023.0427. Online ahead of print.

ABSTRACT

Developing a new drug is a long and expensive process that typically takes 10-15 years and costs billions of dollars. This has led to an increasing interest in drug repositioning, which involves finding new therapeutic uses for existing drugs. Computational methods become an increasingly important tool for identifying associations between drugs and new diseases. Graph- and hypergraph-based approaches are a type of computational method that can be used to identify potential associations between drugs and new diseases. Here, we present a drug repurposing method based on hypergraph neural network for predicting drug-disease association in three stages. First, it constructs a heterogeneous graph that contains drug and disease nodes and links between them; in the second stage, it converts the heterogeneous simple graph to a hypergraph with only disease nodes. This is achieved by grouping diseases that use the same drug into a hyperedge. Indeed, all the diseases that are the common therapeutic goal of a drug are placed on a hyperedge. Finally, a graph neural network is used to predict drug-disease association based on the structure of the hypergraph. This model is more efficient than other methods because it uses a hypergraph to model relationships more effectively than graphs. Furthermore, it constructs the hypergraph using only a drug-disease association matrix, eliminating the need for extensive amounts of data. Experimental results show that the hypergraph-based approach effectively captures complex interrelationships between drugs and diseases, leading to improved accuracy of drug-disease association prediction compared to state-of-the-art methods.

PMID:39648844 | DOI:10.1089/cmb.2023.0427

Cancer Rep (Hoboken). 2024 Dec;7(12):e70072. doi: 10.1002/cnr2.70072.

ABSTRACT

BACKGROUND: Paclitaxel (PTX) has been used as a chemotherapeutic agent for several malignancies, including breast cancer, and efforts to increase the efficiency of PTX are continuous. Previous studies have shown that the voltage-gated K+ channels are over-expressed in breast cancer cell lines; therefore, blocking this type of K+ channel reduces cell proliferation and viability.

AIMS: In this study, FDA-approved 4-aminopyridine (4-AP), a voltage-gated potassium channel blocker, was used in combination with PTX to improve the anticancer activity of PTX in MCF-7 and MDA-MB-231 cell lines.

METHODS AND RESULTS: Viability was determined with trypan blue, a clonogenic assay was performed, and the cell cycle was determined with a flow cytometer and immunochemistry. To gain an insight into the mechanism, intracellular K+ concentration, intracellular Ca2+ (calcium) concentration, and transmembrane potential measurements were made with corresponding fluorescent dyes. The apoptotic cell number was determined using Annexin /PI method by flow cytometer. Viability decreased with combination therapy and the clonogenic assay proved decreased colony formation. The apoptotic cell number was increased after treatment with the combination in both cell lines. Cell cycle measurements showed G1 arrest for both MCF-7 and MDA-MB-231 cell lines upon 4-AP treatment. PTX caused G1 arrest in MCF-7 cells and S phase arrest in MDA-MB-231 cells. Combination treatment caused S phase arrest in MCF-7 cells and S phase and G2/M phase arrest in MDA-MB-231 cells. Intracellular K+ concentration was increased after all treatments in both cell lines. Ca2+ concentration was increased significantly after combination treatment. Depolarization in the transmembrane potential was observed after all treatments in both cell lines.

CONCLUSION: Biophysical parameters like the transmembrane potential and ion fluxes have been defined in cancer progression which can provide new aspects for cancer treatments. This study shows that the combination of 4-AP with PTX is a promising alternative the mechanism of which needs further investigation considering the results obtained for Ca2+, K+, and membrane potential.

PMID:39648339 | DOI:10.1002/cnr2.70072

Biochemistry (Mosc). 2024 Nov;89(11):1868-1888. doi: 10.1134/S0006297924110026.

ABSTRACT

Substance use disorders are associated with structural and functional changes in the neuroendocrine, neuromediator, and neuromodulator systems in brain areas involved in the reward and stress response circuits. Chronic intoxication provokes emergence of somatic diseases and aggravates existing pathologies. Substance use disorders and somatic diseases often exacerbate the clinical courses of each other. Elucidation of biochemical pathways common for comorbidities may serve as a basis for the development of new effective pharmacotherapy agents, as well as drug repurposing. Here, we discussed molecular mechanisms underlying integration of visceral systems into the central mechanisms of drug dependence.

PMID:39647817 | DOI:10.1134/S0006297924110026

J Adv Res. 2024 Dec 6:S2090-1232(24)00560-5. doi: 10.1016/j.jare.2024.12.004. Online ahead of print.

ABSTRACT

BACKGROUND: Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graphs, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM: of review: This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW: This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

PMID:39647635 | DOI:10.1016/j.jare.2024.12.004

Microb Pathog. 2024 Dec 6:107212. doi: 10.1016/j.micpath.2024.107212. Online ahead of print.

ABSTRACT

Candida auris is a global health threat due to its multidrug-resistant nature, particularly in intensive care units, where outbreaks are associated with high mortality rates. The urgency for alternative effective strategies has led to the exploration of combination therapy and drug repurposing Out of the possible drugs known with a potential antifungal effect, sertraline, a selective serotonin reuptake inhibitor widely used on clinical settings has shown promising results. This study aimed to evaluate the antifungal activity of sertraline and voriconazole alone and in combination in a murine model of candidaemia due to C. auris. Immunosuppressed BALB/c mice were infected via intravenous injection with C. auris and then received experimental treatments intraperitoneally for 7 days. The therapeutic efficacy was assessed by determining fungal tissue burden and animal survival. Sertraline exhibited a dose-dependent decrease in fungal burden, with the kidneys showing the most substantial reduction. Combination therapy of sertraline + voriconazole demonstrated an enhanced antifungal effect compared to the monotherapy of both drugs. As far as we know, this preclinical study is the first to evaluate the antifungal activity of sertraline, alone and in combination with an antifungal, against C. auris, representing a possible promissory option for adjuvant treatment of candidaemia due to this organism.

PMID:39647545 | DOI:10.1016/j.micpath.2024.107212

Bioorg Chem. 2024 Dec 7;154:108017. doi: 10.1016/j.bioorg.2024.108017. Online ahead of print.

ABSTRACT

Tyrosine kinase inhibitors (TKIs) represent a pivotal class of targeted therapies in oncology, with multiple generations developed to address diverse molecular targets. Imatinib is the first TKI developed to target the BCR-ABL1 chimeric protein, which is the key driver oncogene implicated in Philadelphia chromosome-positive chronic myeloid leukemia (CML). Several second-generation tyrosine kinase inhibitors (2GTKIs), such as nilotinib, dasatinib, bosutinib, and radotinib (RTB), followed the groundbreaking introduction of imatinib. RTB occupies the unique position of being the least explored member of this class. While nilotinib, dasatinib, and bosutinib have garnered significant attention and extensive research focus, RTB remains relatively uncharted in comparison to its counterparts. Fundamental drug characteristics, such as the pharmacokinetic and pharmacodynamic properties of RTB, remain unavailable in existing sources. Compared to other 2GTKIs, RTB has been less utilized in combinatorial drug studies, and no investigations have been reported on its effects on solid tumors to date. However, the effects of RTB have been studied in acute myeloid leukemia (AML), multiple myeloma (MM), Parkinson's disease, and idiopathic pulmonary fibrosis (IPF). Although RTB has been investigated in some conditions, these studies are still in their preliminary stages and are comparatively lesser than studies on other 2GTKIs. This review is the first attempt that extensively presents a compilation of data on RTB and describes its therapeutic potential against blood and solid tumors. Further investigations on RTB could expand its chemotherapeutic usage in various solid tumors and enhance the possibility of drug repurposing in cancer therapy.

PMID:39647393 | DOI:10.1016/j.bioorg.2024.108017

Mol Divers. 2024 Dec 8. doi: 10.1007/s11030-024-11063-9. Online ahead of print.

ABSTRACT

The PCSK9 protein binds to LDL receptors (LDLR), leading to their degradation and reduced expression on cell surfaces. This decreased the clearance of LDL cholesterol from the bloodstream, thereby increasing the risk of coronary artery diseases. Targeting the PCSK9-LDL receptor interaction is crucial for regulating LDL cholesterol levels and preventing cardiovascular disease. This study aims to screen low molecular weight inhibitors to disrupt the PCSK9-LDLR interaction. We employed a comprehensive approach combining high-throughput virtual screening of DrugBank database, followed by molecular docking studies using CDOCKER and flexible docking methods. The top four lead compounds were further validated through molecular dynamics (MD) simulations and binding free energy calculations using MM-PBSA. Finally, the in vitro assay confirmed that Benazepril and Quinapril exhibited the highest potency as PCSK9-LDLR disruptors among the top candidates. These lead compounds have the potential to be repurposed as lipid-lowering agents for the treatment of cardiovascular diseases, offering a promising therapeutic strategy.

PMID:39645639 | DOI:10.1007/s11030-024-11063-9

Orv Hetil. 2024 Dec 8;165(49):1919-1926. doi: 10.1556/650.2024.33177. Print 2024 Dec 8.

ABSTRACT

A gyógyszer-repozicionálás a gyógyszerfejlesztés különleges, mind gyakrabban alkalmazott útja. Az eljárás eredményeként bizonyos betegségek kezelésére már forgalomba került, illetve a fejlesztés különböző fázisaiban található gyógyszereket, hatóanyagokat új, az eredetitől néha teljesen eltérő indikációval kezdenek sikerrel használni. A közlemény első felében e folyamatról adunk rövid áttekintést, vázoljuk előnyeit és lehetséges buktatóit. Az általános bevezető részt követően számba vesszük a korábban antidiabetikumként forgalomba került gyógyszerek újabb sikeres alkalmazási területeit, majd megemlítünk néhány olyan készítményt, amely mára a diabetes mellitus kezelésében is felhasználhatóvá vált, időközben felismert számottevőbb vércukorszint-csökkentő tulajdonságai révén. Orv Hetil. 2024; 165(49): 1919–1926.

PMID:39645620 | DOI:10.1556/650.2024.33177

Comput Biol Med. 2024 Dec 6;185:109481. doi: 10.1016/j.compbiomed.2024.109481. Online ahead of print.

ABSTRACT

Pancreatic cancer, a malignancy notorious for its late-stage diagnosis and low patient survival rates, remains a formidable global health challenge. The currently available FDA-approved treatments for pancreatic cancer, notably chemotherapeutic agents, exhibit suboptimal efficacy, often accompanied by concerns regarding toxicity. Given the intricate nature of pancreatic cancer pathogenesis and the time-intensive nature of in silico drug discovery approaches, drug repurposing emerges as a compelling strategy to expedite the development of novel therapeutic interventions. In our study, we harnessed transcriptomic data from an exhaustive exploration of four diverse databases, ensuring a rigorous and unbiased analysis of differentially expressed genes, with a particular focus on upregulated genes associated with pancreatic cancer. Leveraging these pancreatic cancer-associated host protein targets, we employed a battery of cutting-edge bioinformatics tools, including Cytoscape STRING, GeneMANIA, Connectivity Map, and NetworkAnalyst, to identify potential small molecule drug candidates and elucidate their interactions. Subsequently, we conducted meticulous docking and redocking simulations for the selected drug-protein target pairs. This rigorous computational approach culminated in the identification of two promising broad-spectrum drug candidates against four pivotal host genes implicated in pancreatic cancer. Our findings strongly advocate for further investigation and preclinical validation of these candidates. Specifically, we propose prioritizing Dasatinib for evaluation against MMP3, MMP9, and EGFR due to their remarkable binding affinities, as well as Pioglitazone against MMP3, MMP2 and MMP9. These discoveries hold great promise in advancing the therapeutic landscape for pancreatic cancer, offering new avenues for improving patient outcomes.

PMID:39644581 | DOI:10.1016/j.compbiomed.2024.109481

Chemphyschem. 2024 Dec 7:e202400751. doi: 10.1002/cphc.202400751. Online ahead of print.

ABSTRACT

The spike protein is a vital target for therapeutic advancement to inhibit viral entrance. Given that the connection between Spike and ACE2 constitutes the initial phase of SARS-CoV-2 pathogenesis, obstructing this interaction presents a promising therapeutic approach. This work aims to find compounds from DrugBank that can modulate the stability of the spike RBD-ACE2 protein-protein complex. Employing a therapeutic repurposing strategy, we conducted molecular docking of over 9000 DrugBank compounds against the Spike RBD-ACE2 complex, on ten variants, including the wild-type. We also evaluated the intricate stability of the RBD-ACE2 proteins by molecular dynamics simulations, hydrogen bond analysis, RMSD analysis, radius of gyration analysis, and the QM-MM approach. We assessed the efficacy of the top ten candidates for each variant as an inhibitor. Our findings demonstrated for the first time that DrugBank small molecules can interact in three distinct modalities inside the extensive protein-protein interface of RBD and ACE2 complexes. The top ten analyses identified specific DrugBank candidates for each variant and molecules capable of binding to multiple variants. This comprehensive computational technique enables the screening and forecasting of hits for any big and shallow protein-protein interface drug targets.

PMID:39644215 | DOI:10.1002/cphc.202400751

J Comput Chem. 2025 Jan 5;46(1):e27518. doi: 10.1002/jcc.27518.

ABSTRACT

Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction. The proposed SeqDPI model extract the relevant drug and protein features from the one dimensional Sequential representation of the dataset considered using optimized CNN networks that deploy convolutions on varying length of amino acid subsequence's to capture hidden pattern, the convolved drug- protein features obtained are then used as an input to L2 penalized feed forward neural network which matches the local residue patterns in protein classes with molecular fingerprints of drugs to predict the binding strength for all drug target pairs. The proposed model reduces the convolution strain typically encountered in existing in silico models that utilize complex 3D structures of drug protein datasets. The result shows that the SeqDPI model achieves a mean square error MSE of (0.167) across cross validation folds, outperforming baseline models such as KronRLS (0.406), Simboost (0.226), and DeepPS (0.214). Additionally, SeqDPI attains a high CI score of 0.9114 on the benchmark KIBA dataset, demonstrating its statistical significance and computational efficiency compared to existing methods. This gives the relevance and effectiveness of SeqDPI model in accurately predicting binding affinities while working with simpler one-dimensional data, making it a robust and computationally cost-effective solution for drug-target interaction prediction.

PMID:39644133 | DOI:10.1002/jcc.27518

PLoS One. 2024 Dec 6;19(12):e0303501. doi: 10.1371/journal.pone.0303501. eCollection 2024.

ABSTRACT

A new round of monkeypox virus has emerged in the United Kingdom since July 2022 and rapidly swept the world. Currently, despite numerous research groups are studying this virus and seeking effective treatments, the information on the open reading frame, inhibitors, and potential targets of monkeypox has not been updated in time, and the comprehension of monkeypox target ligand interactions remains a key challenge. Here, we first summarized and improved the open reading frame information of monkeypox, constructed the monkeypox inhibitor library and potential targets library by database research as well as literature search, combined with advanced protein modeling technologies (Sequence-based and AI algorithms-based homology modeling). In addition, we build monkeypox virus Docking Server, a web server to predict the binding mode between targets and substrate. The open reading frame information, monkeypox inhibitor library, and monkeypox potential targets library are used as the initial files for server docking, providing free interactive tools for predicting ligand interactions of monkeypox targets, potential drug screening, and potential targets search. In addition, the update of the three databases can also effectively promote the study of monkeypox drug inhibition mechanism and provide theoretical guidance for the development of drugs for monkeypox.

PMID:39642129 | DOI:10.1371/journal.pone.0303501

Alzheimers Dement. 2024 Dec 6. doi: 10.1002/alz.14373. Online ahead of print.

ABSTRACT

INTRODUCTION: High microglial heterogeneities hinder the development of microglia-targeted treatment for Alzheimer's disease (AD).

METHODS: We integrated 0.7 million single-nuclei RNA-sequencing transcriptomes from human brains using a variational autoencoder. We predicted AD-relevant microglial subtype-specific transition networks for disease-associated microglia (DAM), tau microglia, and neuroinflammation-like microglia (NIM). We prioritized drugs by specifically targeting microglia-specific transition networks and validated drugs using two independent real-world patient databases.

RESULTS: We identified putative AD molecular drivers (e.g., SYK, CTSB, and INPP5D) in transition networks of DAM and NIM. Via specifically targeting NIM, we identified that usage of ketorolac was associated with reduced AD incidence in both MarketScan (hazard ratio [HR] = 0.89) and INSIGHT (HR = 0.83) Clinical Research Network databases, mechanistically supported by ketorolac-treated transcriptomic data from AD patient induced pluripotent stem cell-derived microglia.

DISCUSSION: This study offers insights into the pathobiology of AD-relevant microglial subtypes and identifies ketorolac as a potential anti-inflammatory treatment for AD.

HIGHLIGHTS: An integrative analysis of ≈ 0.7 million single-nuclei RNA-sequencing transcriptomes from human brains identified Alzheimer's disease (AD)-relevant microglia subtypes. Network-based analysis identified putative molecular drivers (e.g., SYK, CTSB, INPP5D) of transition networks between disease-associated microglia (DAM) and neuroinflammation-like microglia (NIM). Via network-based prediction and population-based validation, we identified that usage of ketorolac (a US Food and Drug Administration-approved anti-inflammatory medicine) was associated with reduced AD incidence in two independent patient databases. Mechanistic observation showed that ketorolac treatment downregulated the Type-I interferon signaling in patient induced pluripotent stem cell-derived microglia, mechanistically supporting its protective effects in real-world patient databases.

PMID:39641322 | DOI:10.1002/alz.14373

Nat Prod Res. 2024 Dec 6:1-13. doi: 10.1080/14786419.2024.2436112. Online ahead of print.

ABSTRACT

The increasing risk of drug-resistant infections and unexpected pandemics like Covid-19 has prompted researchers to explore the area of drug repurposing. Natural products, being a result of the evolutionary optimisation processes can be potential starting points for such drug discovery programs. One such unexplored chemical is terrein, a secondary fungal metabolite. Although discovered in 1935 from Aspergillus terreus, the therapeutic potential of terrein has largely remained undeciphered. Research has primarily been focused on its biosynthetic pathways and its mycotoxic effects. However, in the last two decades, its biological properties including anticancer, anti-inflammatory anti-melanogenic, and bacteriocidal activities have been reported. These reports are preliminary in nature and do not adequately establish its overall therapeutic application. From its structural and therapeutic properties, it can be conjectured that terrein may act as a novel multimodal therapeutic. This comprehensive study reviews the synthesis, production and application aspects of terrein to understand its importance.

PMID:39641157 | DOI:10.1080/14786419.2024.2436112

Antivir Ther. 2024 Dec;29(6):13596535241305536. doi: 10.1177/13596535241305536.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has created an urgent need for effective therapeutic agents. The SARS-CoV-2 Main Protease (Mpro) plays a crucial role in viral replication and immune evasion, making it a key target for drug development. While several studies have explored Mpro inhibition, identifying FDA-approved drugs with potential efficacy remains a critical research focus.

PURPOSE: This study aims to identify FDA-approved drugs that could inhibit SARS-CoV-2 Mpro. Using computational screening, we seek compounds that share structural similarities with a known co-crystallized ligand (PRD_002214) and exhibit strong binding affinity to the enzyme, providing viable candidates for COVID-19 treatment.

RESEARCH DESIGN: A systematic in silico approach was used, screening 3009 FDA-approved drugs. The initial screening focused on structural similarity to PRD_002214 (PDB ID: 6LU7), followed by molecular docking studies to predict binding affinity. Promising compounds were further analyzed through molecular dynamics (MD) simulations to evaluate their stability and interactions with Mpro over 100 ns.

STUDY SAMPLE: Of the 3009 FDA-approved drugs screened, 74 were selected for initial evaluation. After refinement, 28 compounds underwent docking analysis, with eight showing strong binding potential to Mpro.

ANALYSIS: Molecular docking assessed the binding affinity and interaction of the selected compounds with Mpro. MD simulations were conducted on the top compound, Atazanavir, to study its dynamic interactions. MM-GBSA, PLIP, and PCAT analyses were used to validate binding affinity and interactions.

RESULTS: Eight compounds, including Carfilzomib, Atazanavir, Darunavir, and others, exhibited promising binding affinities. Among them, Atazanavir showed the highest binding strength and was selected for further MD simulation studies. These simulations revealed that Atazanavir forms stable interactions with Mpro, demonstrating favorable binding and dynamic stability. The binding affinity was further confirmed through MM-GBSA, PLIP, and PCAT analyses, supporting Atazanavir's potential as an effective Mpro inhibitor.

CONCLUSIONS: In silico results suggest that Atazanavir is a promising candidate for targeting SARS-CoV-2 Mpro, with strong binding affinity and dynamic stability. These findings support its potential as a lead compound for further preclinical and clinical testing, though in vitro and in vivo validation are needed to confirm its therapeutic efficacy against COVID-19.

PMID:39639531 | DOI:10.1177/13596535241305536

Sci Rep. 2024 Dec 5;14(1):30329. doi: 10.1038/s41598-024-82088-1.

ABSTRACT

Breast cancer remains a global health challenge, prompting interest in the anticancer properties of other drugs, including chlorpromazine (CPZ). This study presents a novel approach in breast cancer treatment using laser irradiated CPZ. CPZ dissolved in distilled water, was exposed to 266 nm laser irradiation for varying durations, characterized by UV-Vis and FTIR spectroscopy, followed by drug-likeness and ADME-Tox predictions. In vitro assays evaluated the cytotoxicity and cellular effects on MCF-7 breast cancer cells, and compared with MCF-12 A healthy cell line. Laser irradiation altered CPZ molecular structure resulting in photoproducts with favourable drug-like properties and ADME-Tox profiles. In vitro evaluations demonstrate dose and irradiation time-dependent cytotoxicity against breast cancer cells, and reduced toxicity on healthy cell line. Significant alterations in F-actin organization, and excessive ROS generation were also proved, suggesting the potential of laser-modified CPZ for breast cancer therapy. This study introduces a novel approach to breast cancer treatment through laser irradiated CPZ, highlighting promising advancements in therapy and emphasizing the role of laser-generated compounds.

PMID:39639119 | DOI:10.1038/s41598-024-82088-1

Sci Rep. 2024 Dec 5;14(1):30349. doi: 10.1038/s41598-024-82065-8.

ABSTRACT

Inflammation is a defensive mechanism that safeguards the human body against detrimental stimuli. Within this intricate process, ADAM17, a zinc-dependent metalloprotease, emerges as an indispensable element, fostering the activation of diverse inflammatory and growth factors within the organism. Nonetheless, ADAM17 malfunctions can augment the rate of growth, inflammatory factors, and subsequent damage. Thus, in this study, we examined and repurposed drugs to suppress the activity of ADAM17. To this end, we employed bioinformatics techniques such as molecular docking, molecular dynamics, and pharmacokinetic studies. Five FDA-approved drugs including Raltegravir, Conivaptan, Paclitaxel, Saquinavir, and Venetoclax with the ability to impede the activity of the ADAM17 metalloenzyme were identified. Moreover, these drugs did not include strong zinc-binding functional groups when verified by the ACE functional group finder. However, further in silico analysis has indicated that Raltegravir demonstrates a commendable interaction with the active site amino acids and exhibits the most favorable pharmacokinetic properties compared to others. Considering the results of bioinformatics tools, it can be concluded that Raltegravir as an antiviral drug could be repurposed to prevent severe inflammatory response and tumorigenesis resulting from ADAM17 malfunction.

PMID:39639095 | DOI:10.1038/s41598-024-82065-8

Neurosci Res. 2024 Dec 3:S0168-0102(24)00151-2. doi: 10.1016/j.neures.2024.12.001. Online ahead of print.

ABSTRACT

Spinal cord injury (SCI) results in damage to neural circuits that cause long-term locomotor and sensory disability. The objective of the present study is to evaluate whether a clinical drug, protamine, can be employed as a therapeutic agent for SCI. First, we examined the rescue effect of protamine on dystrophic endballs (DEs) cultured on a chondroitin sulfate (CS) gradient coating. Consequently, axons with DE, which are unable to grow through the CS barrier, resumed growth after protamine treatment and were able to pass through the barrier. In addition, we tested whether protamine resolves the DE phenotype, accumulation of autophagosomes. The results demonstrated that protamine has significantly reduced the density of LC3 in DEs. Subsequently, mice were administered 1mg/kg protamine via the tail vein one week following a contusion injury to the thoracic spinal cord. The hindlimb movements of the mice were evaluated in order to assess the therapeutic effect of protamine. Eleven venous administrations of protamine improved the symptoms. The current study has demonstrated that protamine cancels the CS inhibitory effect on axonal regrowth. Administrations of protamine were observed to alleviate hindlimb motor dysfunction in SCI mice. Our results suggest an effective therapeutic agent for SCI and a possibility for drug repositioning. It would be of interest to see if protamine also exerts a therapeutic effect in brain injury.

PMID:39638151 | DOI:10.1016/j.neures.2024.12.001

SLAS Discov. 2024 Dec 3:100200. doi: 10.1016/j.slasd.2024.100200. Online ahead of print.

ABSTRACT

Ataxia Telangiectasia (A-T) is a rare, autosomal recessive genetic disorder characterized by a variety of symptoms, including progressive neurodegeneration, telangiectasia, immunodeficiency, and an increased susceptibility to cancer. It is caused by bi-allelic mutations impacting a gene encoding a serine/threonine kinase ATM (Ataxia Telangiectasia Mutated), which plays a crucial role in DNA repair and maintenance of genomic stability. The disorder primarily affects the nervous system, leading to a range of neurological issues, including cerebellar ataxia. The cause of neurodegeneration due to mutations in ATM is still an area of investigation, and currently there is no known treatment to slow down or stop the progression of the neurological problems. In this collaboration of the A-T Children's Project (ATCP) with Charles River Discovery, we successfully developed a high-throughput assay using induced pluripotent stem cells (iPSC) from A-T donors to measure DNA damage response (DDR). By measuring the changes in levels of activated phosphorylated CHK2 (p-CHK2), which is a downstream signaling event of ATM, we were able to identify compounds that restore this response in the DDR pathway in A-T derived patient cells. Over 6,000 compounds from small molecule drug repurposing libraries were subsequently screened in the assay developed, leading to identification of several promising in vitro hits. Using the assay developed and the identified hits opens avenues to investigate potential therapeutics for A-T.

PMID:39638147 | DOI:10.1016/j.slasd.2024.100200