Int J Obes (Lond). 2024 Jul 18. doi: 10.1038/s41366-024-01592-6. Online ahead of print.

ABSTRACT

BACKGROUNDS: Genome-wide association studies have identified multiple genetic variants associated with obesity. However, most obesity-associated loci were waiting to be translated into new biological insights. Given the critical role of brain in obesity development, we sought to explore whether obesity-associated genetic variants could be mapped to brain protein abundances.

METHODS: We performed proteome-wide association studies (PWAS) and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity-related traits, including body fat percentage, trunk fat percentage, body mass index, visceral adipose tissue, waist circumference, and waist-to-hip ratio. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level.

RESULTS: By integrating human brain proteomes from discovery (ROSMAP, N = 376) and validation datasets (BANNER, N = 198) with genome-wide summary statistics of obesity-related phenotypes (N ranged from 325,153 to 806,834), we identified 51 genes whose cis-regulated brain protein abundance was associated with obesity. These 51 genes were enriched in 11 metabolic processes, e.g., small molecule metabolic process and metabolic pathways. Fourteen of the 51 genes had high drug repurposing value. Ten of the 51 genes were also associated with obesity at the transcriptome level, suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes.

CONCLUSIONS: Our study provides new insights into the genetic component of human brain protein abundance in obesity. The identified proteins represent promising therapeutic targets for future drug development.

PMID:39025989 | DOI:10.1038/s41366-024-01592-6

Sci Rep. 2024 Jul 18;14(1):16587. doi: 10.1038/s41598-024-67163-x.

ABSTRACT

Drug repurposing aims to find new therapeutic applications for existing drugs in the pharmaceutical market, leading to significant savings in time and cost. The use of artificial intelligence and knowledge graphs to propose repurposing candidates facilitates the process, as large amounts of data can be processed. However, it is important to pay attention to the explainability needed to validate the predictions. We propose a general architecture to understand several explainable methods for graph completion based on knowledge graphs and design our own architecture for drug repurposing. We present XG4Repo (eXplainable Graphs for Repurposing), a framework that takes advantage of the connectivity of any biomedical knowledge graph to link compounds to the diseases they can treat. Our method allows methapaths of different types and lengths, which are automatically generated and optimised based on data. XG4Repo focuses on providing meaningful explanations to the predictions, which are based on paths from compounds to diseases. These paths include nodes such as genes, pathways, side effects, or anatomies, so they provide information about the targets and other characteristics of the biomedical mechanism that link compounds and diseases. Paths make predictions interpretable for experts who can validate them and use them in further research on drug repurposing. We also describe three use cases where we analyse new uses for Epirubicin, Paclitaxel, and Predinisone and present the paths that support the predictions.

PMID:39025897 | DOI:10.1038/s41598-024-67163-x

Assay Drug Dev Technol. 2024 Jul;22(5):265-275. doi: 10.1089/adt.2024.047. Epub 2024 Jun 11.

NO ABSTRACT

PMID:39024477 | DOI:10.1089/adt.2024.047

PLoS One. 2024 Jul 18;19(7):e0304425. doi: 10.1371/journal.pone.0304425. eCollection 2024.

ABSTRACT

COVID-19 caused by SARS-CoV-2 is a global health issue. It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lung diseases stimulate each other, and associated candidate drug molecules. We identified both SARS-CoV-2 infections and different lung diseases (Asthma, Tuberculosis, Cystic Fibrosis, Pneumonia, Emphysema, Bronchitis, IPF, ILD, and COPD) causing top-ranked 11 shared genes (STAT1, TLR4, CXCL10, CCL2, JUN, DDX58, IRF7, ICAM1, MX2, IRF9 and ISG15) as the hub of the shared differentially expressed genes (hub-sDEGs). The gene ontology (GO) and pathway enrichment analyses of hub-sDEGs revealed some crucial common pathogenetic processes of SARS-CoV-2 infections and different lung diseases. The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. Therefore, the findings of this study might be useful resources for the diagnosis and therapies of COVID-19 patients who are also suffering from one or more lung diseases.

PMID:39024368 | DOI:10.1371/journal.pone.0304425

J Virol. 2024 Jul 18:e0061824. doi: 10.1128/jvi.00618-24. Online ahead of print.

ABSTRACT

Zika virus (ZIKV) is a re-emerging mosquito-borne flavivirus that has been associated with congenital neurological defects in fetuses born to infected mothers. At present, no vaccine or antiviral therapy is available to combat this devastating disease. Repurposing drugs that target essential host factors exploited by viruses is an attractive therapeutic approach. Here, we screened a panel of clinically approved small-molecule kinase inhibitors for their antiviral effects against a clinical isolate of ZIKV and thoroughly characterized their mechanisms of action. We found that the Raf kinase inhibitors Dabrafenib and Regorafenib potently impair the replication of ZIKV, but not that of its close relative dengue virus. Time-of-addition experiments showed that both inhibitors target ZIKV infection at post-entry steps. We found that Dabrafenib, but not Regorafenib, interfered with ZIKV genome replication by impairing both negative- and positive-strand RNA synthesis. Regorafenib, on the other hand, altered steady-state viral protein levels, viral egress, and blocked NS1 secretion. We also observed Regorafenib-induced ER fragmentation in ZIKV-infected cells, which might contribute to its antiviral effects. Because these inhibitors target different steps of the ZIKV infection cycle, their use in combination therapy may amplify their antiviral effects which could be further explored for future therapeutic strategies against ZIKV and possibly other flaviviruses.

IMPORTANCE: There is an urgent need to develop effective therapeutics against re-emerging arboviruses associated with neurological disorders like Zika virus (ZIKV). We identified two FDA-approved kinase inhibitors, Dabrafenib and Regorafenib, as potent inhibitors of contemporary ZIKV strains at distinct stages of infection despite overlapping host targets. Both inhibitors reduced viral titers by ~1 to 2 log10 (~10-fold to 100-fold) with minimal cytotoxicity. Furthermore, we show that Dabrafenib inhibits ZIKV RNA replication whereas Regorafenib inhibits ZIKV translation and egress. Regorafenib has the added benefit of limiting NS1 secretion, which contributes to the pathogenesis and disease progression of several flaviviruses. Because these inhibitors affect distinct post-entry steps of ZIKV infection, their therapeutic potential may be amplified by combination therapy and likely does not require prophylactic administration. This study provides further insight into ZIKV-host interactions and has implications for the development of novel antivirals against ZIKV and possibly other flaviviruses.

PMID:39023323 | DOI:10.1128/jvi.00618-24

Hepatol Commun. 2024 Jul 18;8(8):e0480. doi: 10.1097/HC9.0000000000000480. eCollection 2024 Aug 1.

ABSTRACT

BACKGROUND: The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) has increased in recent decades. Approximately 25% of patients with MASLD progress to metabolic dysfunction-associated steatohepatitis, which is characterized by hepatic steatosis plus hepatocyte damage, inflammation, and fibrosis. We previously reported that Neurotropin (NTP), a drug used for relieving pain in Japan and China, inhibits lipid accumulation in hepatocytes by preventing mitochondrial dysfunction. We hypothesized that inhibiting hepatic steatosis and inflammation by NTP can be an effective strategy for treating MASLD and tested this hypothesis in a MASLD mouse model.

METHODS: Six-week-old C57BL/6NJ male mice were fed a normal diet and normal drinking water or a high-fat diet with high fructose/glucose water for 12 weeks. During the last 6 weeks, the mice were also given high-dose NTP, low-dose NTP, or control treatment. Histologic, biochemical, and functional tests were conducted. MitoPlex, a new proteomic platform, was used to measure mitochondrial proteins, as mitochondrial dysfunction was previously reported to be associated with MASLD progression.

RESULTS: NTP inhibited the development of hepatic steatosis, injury, inflammation, and fibrosis induced by feeding a high-fat diet plus high fructose/glucose in drinking water. NTP also inhibited HSC activation. MitoPlex analysis revealed that NTP upregulated the expression of mitochondrial proteins related to oxidative phosphorylation, the tricarboxylic acid cycle, mitochondrial dynamics, and fatty acid transport.

CONCLUSIONS: Our results indicate that NTP prevents the development of hepatic steatosis, injury, and inflammation by preserving mitochondrial function in the liver and inhibits liver fibrosis by suppressing HSC activation. Thus, repurposing NTP may be a beneficial option for treating MASLD/metabolic dysfunction-associated steatohepatitis.

PMID:39023282 | DOI:10.1097/HC9.0000000000000480

Clin Transl Sci. 2024 Jul;17(7):e13865. doi: 10.1111/cts.13865.

ABSTRACT

The urgent need for safe, efficacious, and accessible drug treatments to treat coronavirus disease 2019 (COVID-19) prompted a global effort to evaluate drug repurposing opportunities. Pyronaridine and amodiaquine are both components of approved antimalarials with in vitro activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In vitro activity does not always translate to clinical efficacy across a therapeutic dose range. This study applied available, verified, physiologically based pharmacokinetic (PBPK) models for pyronaridine, amodiaquine, and its active metabolite N-desethylamodiaquine (DEAQ) to predict drug concentrations in lung tissue relative to plasma or blood in the default healthy virtual population. Lung exposures were compared to published data across the reported range of in vitro EC50 values against SARS-CoV-2. In the multicompartment permeability-limited PBPK model, the predicted total Cmax in lung mass for pyronaridine was 34.2 μM on Day 3, 30.5-fold greater than in blood (1.12 μM) and for amodiaquine was 0.530 μM, 8.83-fold greater than in plasma (0.060 μM). In the perfusion-limited PBPK model, the DEAQ predicted total Cmax on Day 3 in lung mass (30.2 μM) was 21.4-fold greater than for plasma (1.41 μM). Based on the available in vitro data, predicted drug concentrations in lung tissue for pyronaridine and DEAQ, but not amodiaquine, appeared sufficient to inhibit SARS-CoV-2 replication. Simulations indicated standard dosing regimens of pyronaridine-artesunate and artesunate-amodiaquine have potential to treat COVID-19. These findings informed repurposing strategies to select the most relevant compounds for clinical investigation in COVID-19. Clinical data for model verification may become available from ongoing clinical studies.

PMID:39020517 | DOI:10.1111/cts.13865

Sci Rep. 2024 Jul 17;14(1):16562. doi: 10.1038/s41598-024-67594-6.

ABSTRACT

Due to considerable global prevalence and high recurrence rate, the pursuit of effective new medication for epilepsy treatment remains an urgent and significant challenge. Drug repurposing emerges as a cost-effective and efficient strategy to combat this disorder. This study leverages the transformer-based deep learning methods coupled with molecular binding affinity calculation to develop a novel in-silico drug repurposing pipeline for epilepsy. The number of candidate inhibitors against 24 target proteins encoded by gain-of-function genes implicated in epileptogenesis ranged from zero to several hundreds. Our pipeline has repurposed the medications with most anti-epileptic drugs and nearly half psychiatric medications, highlighting the effectiveness of our pipeline. Furthermore, Lomitapide, a cholesterol-lowering drug, first emerged as particularly noteworthy, exhibiting high binding affinity for 10 targets and verified by molecular dynamics simulation and mechanism analysis. These findings provided a novel perspective on therapeutic strategies for other central nervous system disease.

PMID:39020064 | DOI:10.1038/s41598-024-67594-6

Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2024 Apr 28;49(4):611-620. doi: 10.11817/j.issn.1672-7347.2024.230442.

ABSTRACT

OBJECTIVES: The antimicrobial resistance of Staphylococcus aureus (S. aureus) has become a challenge in the treatment of infectious diseases. It is of great clinical value to discovery effective antimicrobial agents against multi-drug resistant S. aureus and its biofilms. This study aims to explore the antibacterial activity of the antiparasitic drug closantel against methicillin-resistant S. aureus and its biofilms through drug repurposing.

METHODS: The sensitivity of S. aureus to closantel was assessed using microbroth dilution and disk diffusion methods. The bacteriostatic and bactericidal activities of closantel were determined by time-kill curves and colony count. Scanning electron microscopy combined with SYTOX Green and DiSC3(5) fluorescence probes were used to study the bactericidal mechanism of closantel. The influence of resistance was assessed by continuous exposure to sub-inhibitory concentrations of closantel. The anti-biofilm activity was evaluated using 96-well plates and crystal violet staining, and cytotoxicity was measured using the CCK-8 assay.

RESULTS: The minimal inhibitory concentration (MIC) of closantel for both methicillin-sensitive and methicillin-resistant S. aureus ranged from 0.125 to 1.000 μg/mL. Disk diffusion tests showed that 80 μg of closantel created an inhibition zone, which increased in diameter with higher drug amounts. Sub-inhibitory concentrations (0.031 μg/mL) of closantel significantly inhibited S. aureus proliferation, reducing bacterial turbidity from 0.26±0.00 to 0.11±0.01 (t=16.06, P<0.001), with stronger inhibition at higher concentrations. Closantel at 0.25×MIC inhibited S. aureus proliferation for 12 hours, while 1×MIC inhibited it for over 24 hours, with the number of viable bacteria decreasing as the drug concentration increased. Mechanistic studies indicated that closantel effectively disrupted the integrity of S. aureus cell membranes, significantly increasing SYTOX Green and DiSC3(5) fluorescence intensity. Even after 25 days of continuous exposure to sub-inhibitory concentrations of closantel, no resistance developed. Closantel at 0.0625 μg/mL significantly inhibited biofilm formation, reducing it from 1.29±0.16 to 0.62±0.04 (t=11.62, P<0.001), showing a clear dose-dependent effect. Closantel at 2 μg/mL also significantly eradicated established biofilms, reducing biofilm mass from 1.62±0.34 to 0.51±0.39 (t=4.84, P<0.01). Additionally, closantel exhibited extremely low cytotoxicity, with half-maximal lethal concentrations for HepG2 liver cancer cells and normal LO2 liver cells both exceeding 64 μg/mL.

CONCLUSIONS: Closantel exhibits strong antibacterial activity against S. aureus and its biofilm with low cytotoxicity against human cells, making it a promising candidate for new therapeutic strategies against S. aureus-related infections.

PMID:39019790 | DOI:10.11817/j.issn.1672-7347.2024.230442

Hypertension. 2024 Aug;81(8):1766-1775. doi: 10.1161/HYPERTENSIONAHA.123.21858. Epub 2024 Jun 19.

ABSTRACT

BACKGROUND: We investigated the potential impact of antihypertensive drugs for atrial fibrillation (AF) prevention through a drug target Mendelian randomization study to avoid the potential limitations of clinical studies.

METHODS: Validated published single-nucleotide polymorphisms (SNPs) that mimic the action of 12 antihypertensive drug classes, including alpha-adrenoceptor blockers, adrenergic neuron blockers, angiotensin-converting enzyme inhibitors, angiotensin-II receptor blockers, beta-adrenoceptor blockers, centrally acting antihypertensive drugs, calcium channel blockers, loop diuretics, potassium-sparing diuretics and mineralocorticoid receptor antagonists, renin inhibitors, thiazides and related diuretic agents, and vasodilators were used. We estimated, via their corresponding gene and protein targets, the downstream effect of these drug classes to prevent AF via systolic blood pressure using 2-sample Mendelian randomization analyses. The SNPs were extracted from 2 European genome-wide association studies for the drug classes (n=317 754; n=757 601) and 1 European genome-wide association study for AF (n=1 030 836).

RESULTS: Drug target Mendelian randomization analyses supported the significant preventive causal effects of lowering systolic blood pressure per 10 mm Hg via alpha-adrenoceptor blockers (n=11 SNPs; odds ratio [OR], 0.34 [95% CI, 0.21-0.56]; P=2.74×10-05), beta-adrenoceptor blockers (n=17 SNPs; OR, 0.52 [95% CI, 0.35-0.78]; P=1.62×10-03), calcium channel blockers (n=49 SNPs; OR, 0.50 [95% CI, 0.36-0.70]; P=4.51×10-05), vasodilators (n=19 SNPs; OR, 0.53 [95% CI, 0.34-0.84]; P=7.03×10-03), and all 12 antihypertensive drug classes combined (n=158 SNPs; OR, 0.64 [95% CI, 0.54-0.77]; P=8.50×10-07) on AF risk.

CONCLUSIONS: Our results indicated that lowering systolic blood pressure via protein targets of various antihypertensive drugs seems promising for AF prevention. Our findings inform future clinical trials and have implications for repurposing antihypertensive drugs for AF prevention.

PMID:39018378 | DOI:10.1161/HYPERTENSIONAHA.123.21858

Clin Cancer Res. 2024 Jul 17. doi: 10.1158/1078-0432.CCR-23-3980. Online ahead of print.

ABSTRACT

PURPOSE: Systemic treatments given to non-small cell lung cancer (NSCLC) patients are often ineffective due to drug resistance. In the present study, we investigated patient-derived tumor organoids (PDTOs) and matched tumor tissues from surgically treated NSCLC patients to identify drug repurposing targets to overcome resistance towards standard-of-care platinum-based doublet chemotherapy.

EXPERIMENTAL DESIGN: PDTOs were established from ten prospectively enrolled non-metastatic NSCLC patients from resected tumors. PDTOs were compared with matched tumor tissues by histopathology/immunohistochemistry, whole exome and transcriptome sequencing. PDTO growths and drug responses were determined by measuring 3D tumoroid volumes, cell viability, and proliferation/apoptosis. Differential gene expression analysis identified drug-repurposing targets. Validations were performed with internal/external NSCLC patient data sets. NSCLC cell lines were used for aldo-keto reductase 1B10 (AKR1B10) knockdown studies and xenograft models to determine the intratumoral bioavailability of epalrestat.

RESULTS: PDTOs retained histomorphology and pathological biomarker expression, mutational/transcriptomic signatures, and cellular heterogeneity of the matched tumor tissues. Five (50%) PDTOs were chemoresistant towards carboplatin/paclitaxel. Chemoresistant PDTOs and matched tumor tissues demonstrated overexpression of AKR1B10. Epalrestat, an orally available AKR1B10 inhibitor in clinical use for diabetic polyneuropathy, was repurposed to overcome chemoresistance of PDTOs. In vivo efficacy of epalrestat to overcome drug resistance corresponded to intratumoral epalrestat levels.

CONCLUSIONS: PDTOs are efficient preclinical models recapitulating the tumor characteristics and are suitable for drug testing. AKR1B10 can be targeted by repurposing epalrestat to overcome chemoresistance in NSCLC. Epalrestat has the potential to advance to clinical trials in drug-resistant NSCLC patients due to favorable toxicity, pharmacological profile, and bioavailability.

PMID:39017606 | DOI:10.1158/1078-0432.CCR-23-3980

Front Immunol. 2024 Jul 1;15:1409458. doi: 10.3389/fimmu.2024.1409458. eCollection 2024.

ABSTRACT

Current treatments of eosinophilic chronic rhinosinusitis (ECRS) involve corticosteroids with various adverse effects and costly therapies such as dupilumab, highlighting the need for improved treatments. However, because of the lack of a proper mouse ECRS model that recapitulates human ECRS, molecular mechanisms underlying this disease are incompletely understood. ECRS is often associated with aspirin-induced asthma, suggesting that dysregulation of lipid mediators in the nasal mucosa may underlie ECRS pathology. We herein found that the expression of microsomal PGE synthase-1 (encoded by PTGES) was significantly lower in the nasal mucosa of ECRS patients than that of non-ECRS subjects. Histological, transcriptional, and lipidomics analyses of Ptges-deficient mice revealed that defective PGE2 biosynthesis facilitated eosinophil recruitment into the nasal mucosa, elevated expression of type-2 cytokines and chemokines, and increased pro-allergic and decreased anti-allergic lipid mediators following challenges with Aspergillus protease and ovalbumin. A nasal spray containing agonists for the PGE2 receptor EP2 or EP4, including omidenepag isopropyl that has been clinically used for treatment of glaucoma, markedly reduced intranasal eosinophil infiltration in Ptges-deficient mice. These results suggest that the present model using Ptges-deficient mice is more relevant to human ECRS than are previously reported models and that eosinophilic inflammation in the nasal mucosa can be efficiently blocked by activation of the PGE2-EP2 pathway. Furthermore, our findings suggest that drug repositioning of omidenepag isopropyl may be useful for treatment of patients with ECRS.

PMID:39015572 | PMC:PMC11250097 | DOI:10.3389/fimmu.2024.1409458

BMC Chem. 2024 Jul 16;18(1):132. doi: 10.1186/s13065-024-01233-z.

ABSTRACT

The search for new molecules targeting SARS-CoV-2 has been a priority since 2020. The continuous evolution of new mutants increases the need for more research in the area. One way to find new leads is to repurpose existing drugs and molecules against the required target. Here, we present the in vitro and in silico screening of ten previously synthesized and reported compounds as anti-COVID 19 agents. The compounds were screened in vitro against VERO-E6 cells to find their Cytotoxic Concentration (CC50) and their Inhibitory Concentration (IC50). Compounds 1, 2, and 5 revealed a promising anti-SARS-CoV-2 of (IC50 = 2.4, 11.2 and 2.8 µM), respectively while compounds 3 and 7 showed moderate activity of (IC50 = 17.8 and 26.1 µM) compared to Chloroquine which showed an IC50 of 24.9 µM. Among tested compounds, 1 showed the highest selectivity (CC50/IC50) of 192.8. Docking, molecular dynamics and ADME studies were done to investigate potential interactions between compounds and SARS-CoV-2 targets as well as to study the possibility of using them as lead compounds.

PMID:39014447 | DOI:10.1186/s13065-024-01233-z

Protein J. 2024 Jul 16. doi: 10.1007/s10930-024-10217-w. Online ahead of print.

ABSTRACT

Protein conformation is affected by interaction of several small molecules resulting either stabilization or disruption depending on the nature of the molecules. In our earlier communication, Hg2+ was known to disrupt the native structure of α-Cgn A leading to aggregation (Ansari, N.K., Rais, A. & Naeem, A. Methotrexate for Drug Repurposing as an Anti-Aggregatory Agent to Mercuric Treated α-Chymotrypsinogen-A. Protein J (2024). https://doi.org/10.1007/s10930-024-10187-z ). Accumulation of β-rich aggregates in the living system is found to be linked with copious number of disorders. Here, we have investigated the effect of varying concentration of doxorubicin (DOX) i.e. 0-100 µM on the preformed aggregates of α-Cgn A upon incubation with 120 µM Hg2+. The decrease in the intrinsic fluorescence and enzyme activity with respect to increase in the Hg2+ concentration substantiate the formation of aggregates. The DOX showed the dose dependent decrease in the ThT fluorescence, turbidity and RLS measurements endorsing the dissolution of aggregates which were consistent with red shift in ANS, confirming the breakdown of aggregates. The α-Cgn A has 30% α-helical content which decreases to 3% in presence of Hg2+. DOX increased the α-helicity to 28% confirming its anti-aggregatory potential. The SEM validates the formation of aggregates with Hg2+ and their dissolution upon incubation with the DOX. Hemolysis assay checked the cytotoxicity of α-Cgn A aggregates. Docking revealed that the DOX interacted Lys203, Cys201, Cys136, Ser159, Leu10, Trp207, Val137 and Thr134 of α-Cgn A through hydrophobic interactions and Gly133, Thr135 and Lys202 forms hydrogen bonds.

PMID:39014260 | DOI:10.1007/s10930-024-10217-w

Nanomedicine (Lond). 2024 Jul 16:1-15. doi: 10.1080/17435889.2024.2364585. Online ahead of print.

ABSTRACT

Aim: The study explores the synergistic potential of atorvastatin (ATR) and quercetin (QUER)- loaded solid lipid nanoparticles (SLN) in combating breast cancer. Materials & methods: SLNs were synthesized using a high-shear homogenization method and optimized using Box-Behnken design. The SLNs were characterized and evaluated for their in vitro anticancer activity. Results: The optimized SLN exhibited narrow size distribution (PDI = 0.338 ± 0.034), a particle size of 72.5 ± 6.5 nm, higher entrapment efficiency (<90%), sustained release and spherical surface particles. The in vitro cytotoxicity studies showed a significant reduction in IC50 values on MDA-MB-231 cell lines. Conclusion: We report a novel strategy of repurposing well-known drugs and encapsulating them into SLNs as a promising drug-delivery system against breast cancer.

PMID:39012199 | DOI:10.1080/17435889.2024.2364585

Nat Commun. 2024 Jul 15;15(1):5943. doi: 10.1038/s41467-024-50033-5.

ABSTRACT

Inherited retinopathies are devastating diseases that in most cases lack treatment options. Disease-modifying therapies that mitigate pathophysiology regardless of the underlying genetic lesion are desirable due to the diversity of mutations found in such diseases. We tested a systems pharmacology-based strategy that suppresses intracellular cAMP and Ca2+ activity via G protein-coupled receptor (GPCR) modulation using tamsulosin, metoprolol, and bromocriptine coadministration. The treatment improves cone photoreceptor function and slows degeneration in Pde6βrd10 and RhoP23H/WT retinitis pigmentosa mice. Cone degeneration is modestly mitigated after a 7-month-long drug infusion in PDE6A-/- dogs. The treatment also improves rod pathway function in an Rpe65-/- mouse model of Leber congenital amaurosis but does not protect from cone degeneration. RNA-sequencing analyses indicate improved metabolic function in drug-treated Rpe65-/- and rd10 mice. Our data show that catecholaminergic GPCR drug combinations that modify second messenger levels via multiple receptor actions provide a potential disease-modifying therapy against retinal degeneration.

PMID:39009597 | DOI:10.1038/s41467-024-50033-5

ACS Omega. 2024 Jun 27;9(27):29870-29883. doi: 10.1021/acsomega.4c03796. eCollection 2024 Jul 9.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) affects an estimated global population of around 3 million individuals. IPF is a medical condition with an unknown cause characterized by the formation of scar tissue in the lungs, leading to progressive respiratory disease. Currently, there are only two FDA-approved small molecule drugs specifically for the treatment of IPF and this has created a demand for the rapid development of drugs for IPF treatment. Moreover, denovo drug development is time and cost-intensive with less than a 10% success rate. Drug repurposing currently is the most feasible option for rapidly making the drugs to market for a rare and sporadic disease. Normally, the repurposing of drugs begins with a screening of FDA-approved drugs using computational tools, which results in a low hit rate. Here, an integrated machine learning-based drug repurposing strategy is developed to significantly reduce the false positive outcomes by introducing the predock machine-learning-based predictions followed by literature and GSEA-assisted validation and drug pathway prediction. The developed strategy is deployed to 1480 FDA-approved drugs and to drugs currently in a clinical trial for IPF to screen them against "TGFB1", "TGFB2", "PDGFR-a", "SMAD-2/3", "FGF-2", and more proteins resulting in 247 total and 27 potentially repurposable drugs. The literature and GSEA validation suggested that 72 of 247 (29.14%) drugs have been tried for IPF, 13 of 247 (5.2%) drugs have already been used for lung fibrosis, and 20 of 247 (8%) drugs have been tested for other fibrotic conditions such as cystic fibrosis and renal fibrosis. Pathway prediction of the remaining 142 drugs was carried out resulting in 118 distinct pathways. Furthermore, the analysis revealed that 29 of 118 pathways were directly or indirectly involved in IPF and 11 of 29 pathways were directly involved. Moreover, 15 potential drug combinations are suggested for showing a strong synergistic effect in IPF. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating IPF and other related conditions.

PMID:39005763 | PMC:PMC11238209 | DOI:10.1021/acsomega.4c03796

bioRxiv [Preprint]. 2024 Jul 5:2024.07.03.601957. doi: 10.1101/2024.07.03.601957.

ABSTRACT

SUMMARY: The binding interactions between small molecules and proteins are the basis of cellular functions. Yet, experimental data available regarding compound-protein interaction is not harmonized into a single entity but rather scattered across multiple institutions, each maintaining databases with different formats. Extracting information from these multiple sources remains challenging due to data heterogeneity. Here, we present CPIExtract (Compound-Protein Interaction Extract), a tool to interactively extract experimental binding interaction data from multiple databases, perform filtering, and harmonize the resulting information, thus providing a gain of compound-protein interaction data. When compared to a single source, DrugBank, we show that it can collect more than 10 times the amount of annotations. The end-user can apply custom filtering to the aggregated output data and save it in any generic tabular file suitable for further downstream tasks such as network medicine analyses for drug repurposing and cross-validation of deep learning models.

AVAILABILITY: CPIExtract is an open-source Python package under an MIT license. CPIExtract can be downloaded from https://github.com/menicgiulia/CPIExtract and https://pypi.org/project/cpiextract . The package can run on any standard desktop computer or computing cluster.

PMID:39005430 | PMC:PMC11245042 | DOI:10.1101/2024.07.03.601957

bioRxiv [Preprint]. 2024 Jul 5:2024.07.03.601339. doi: 10.1101/2024.07.03.601339.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers.

RESULTS: We annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimer's Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves.

CONCLUSION: The ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance AD's disease research and treatment strategies.

PMID:39005357 | PMC:PMC11245034 | DOI:10.1101/2024.07.03.601339

bioRxiv [Preprint]. 2024 Jul 3:2024.07.02.601689. doi: 10.1101/2024.07.02.601689.

ABSTRACT

Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in the regulation of key cellular processes might affect LUAD risk and survival outcomes, we built individual (person)-specific gene regulatory networks integrating gene expression, transcription factor protein-protein interaction, and sequence motif data, using PANDA/LIONESS algorithms, for both non-cancerous lung tissue samples from the Genotype Tissue Expression (GTEx) project and LUAD samples from The Cancer Genome Atlas (TCGA). In GTEx, we found that pathways involved in cell proliferation and immune response are increasingly targeted by regulatory transcription factors with age; these aging-associated alterations are accelerated by tobacco smoking and resemble oncogenic shifts in the regulatory landscape observed in LUAD and suggests that dysregulation of aging pathways might be associated with an increased risk of LUAD. Comparing normal adjacent samples from individuals with LUAD with healthy lung tissue samples from those without LUAD, we found that aging-associated genes show greater aging-biased targeting patterns in younger individuals with LUAD compared to their healthy counterparts of similar age, a pattern suggestive of age acceleration. This implies that an accelerated aging process may be responsible for tumor incidence in younger individuals. Using drug repurposing tool CLUEreg, we found small molecule drugs with potential geroprotective effects that may alter the accelerating aging profiles we found. We also observed that, in contrast to chronological age, a network-informed aging signature was associated with survival and response to chemotherapy in LUAD.

PMID:39005266 | PMC:PMC11244978 | DOI:10.1101/2024.07.02.601689