Patterns (N Y). 2024 Oct 31;5(11):101093. doi: 10.1016/j.patter.2024.101093. eCollection 2024 Nov 8.

ABSTRACT

Computational analyses of transcriptomic data have dramatically improved our understanding of complex diseases. However, such approaches are limited by small sample sets of disease-affected material. We asked if a variational autoencoder trained on large groups of healthy human RNA sequencing (RNA-seq) data can capture the fundamental gene regulation system and generalize to unseen disease changes. Importantly, we found this model to successfully compress unseen transcriptomic changes from 25 independent disease datasets. We decoded disease-specific signals from the latent space and found them to contain more disease-specific genes than the corresponding differential expression analysis in 20 of 25 cases. Finally, we matched these disease signals with known drug targets and extracted sets of known and potential pharmaceutical candidates. In summary, our study demonstrates how data-driven representation learning enables the arithmetic deconstruction of the latent space, facilitating the dissection of disease mechanisms and drug targets.

PMID:39568475 | PMC:PMC11573900 | DOI:10.1016/j.patter.2024.101093

Expert Opin Drug Discov. 2024 Nov 21:1-15. doi: 10.1080/17460441.2024.2430955. Online ahead of print.

ABSTRACT

INTRODUCTION: Mapping the interactions between pharmaceutical compounds and their molecular targets is a fundamental aspect of drug discovery and repurposing. Drug-target interactions are important for elucidating mechanisms of action and optimizing drug efficacy and safety profiles. Several computational methods have been developed to systematically predict drug-target interactions. However, computational and experimental validation of the drug-target predictions greatly vary across the studies.

AREAS COVERED: Through a PubMed query, a corpus comprising 3,286 articles on drug-target interaction prediction published within the past decade was covered. Natural language processing was used for automated abstract classification to study the evolution of computational methods, validation strategies and performance assessment metrics in the 3,286 articles. Additionally, a manual analysis of 259 studies that performed experimental validation of computational predictions revealed prevalent experimental protocols.

EXPERT OPINION: Starting from 2014, there has been a noticeable increase in articles focusing on drug-target interaction prediction. Docking and regression stands out as the most commonly used techniques among computational methods, and cross-validation is frequently employed as the computational validation strategy. Testing the predictions using multiple, orthogonal validation strategies is recommended and should be reported for the specific target prediction applications. Experimental validation remains relatively rare and should be performed more routinely to evaluate biological relevance of predictions.

PMID:39568436 | DOI:10.1080/17460441.2024.2430955

Br J Haematol. 2024 Nov 20. doi: 10.1111/bjh.19912. Online ahead of print.

ABSTRACT

This commentary discusses the therapeutic potential of drug repurposing in sickle cell disease, highlighting the efficacy of hydroxyurea in enhancing fetal haemoglobin and the work of Raz et al. discussing the potential of using memantine for improving cognitive function, while emphasizing the need for further research. Commentary on: Raz et al. Memantine treatment in sickle cell disease: A 1-year study of its effects on cognitive functions and neural processing. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19866.

PMID:39568202 | DOI:10.1111/bjh.19912

BMC Med. 2024 Nov 20;22(1):547. doi: 10.1186/s12916-024-03760-x.

ABSTRACT

BACKGROUND: We systematically reviewed Mendelian randomization (MR) studies and summarized evidence on the potential effects of different antihypertensive drugs on health.

METHODS: We searched PubMed and Embase for MR studies evaluating the effects of antihypertensive drug classes on health outcomes until 22 May 2024. We extracted data on study characteristics and findings, assessed study quality, and compared the evidence with that from randomized controlled trials (RCTs).

RESULTS: We identified 2643 studies in the search, of which 37 studies were included. These studies explored a wide range of health outcomes including cardiovascular diseases and their risk factors, psychiatric and neurodegenerative diseases, cancer, immune function and infection, and other outcomes. There is strong evidence supporting the protective effects of genetically proxied antihypertensive drugs on cardiovascular diseases. We found strong protective effects of angiotensin-converting enzyme (ACE) inhibitors on diabetes whereas beta-blockers showed adverse effects. ACE inhibitors might increase the risk of psoriasis, schizophrenia, and Alzheimer's disease but did not affect COVID-19. There is strong evidence that ACE inhibitors and calcium channel blockers (CCBs) are beneficial for kidney and immune function, and CCBs showed a safe profile for disorders of pregnancy. Most studies have high quality. RCT evidence supports the beneficial effects of ACE inhibitors and CCBs on stroke, diabetes, and kidney function. However, there is a lack of reliable RCTs to confirm the associations with other diseases.

CONCLUSIONS: Evidence of the benefits and off-target effects of antihypertensive drugs contribute to clinical decision-making, pharmacovigilance, and the identification of drug repurposing opportunities.

PMID:39567981 | DOI:10.1186/s12916-024-03760-x

Comput Biol Med. 2024 Nov 19;184:109415. doi: 10.1016/j.compbiomed.2024.109415. Online ahead of print.

ABSTRACT

Gastric cancer (GC) is a common cancer worldwide. Therefore, searching for effective treatments is essential, and drug repositioning can be a promising strategy to find new potential drugs for GC therapy. For the first time, we sought to identify molecular alterations and validate new mechanisms related to Mebendazole (MBZ) treatment in GC cells through transcriptome analysis using microarray technology. Data revealed 1066 differentially expressed genes (DEGs), of which 345 (2.41 %) genes were upregulated, 721 (5.04 %) genes were downregulated, and 13,231 (92.54 %) genes remained unaltered after MBZ exposure. The overexpressed genes identified were CCL2, IL1A, and CDKN1A. In contrast, the H3C7, H3C11, and H1-5 were the top 3 underexpressed genes. Gene set enrichment analysis (GSEA) identified 8 pathways significantly overexpressed in the treated group (p < 0.05 and FDR<0.25). The validation of the expression of top desregulated genes by RT-qPCR confirmed the transcriptome results, where MBZ increased the CCL2, IL1A, and CDKN1A and reduced the H3C7, H3C11, and H1-5 transcript levels. Expression analysis in samples from TCGA databases correlated that the lower ILI1A and higher H3C11 and H1-5 gene expression are associated with decreased overall survival rates in patients with GC, indicating that MBZ treatment can improve the prognosis of patients. Thus, the data demonstrated that the drug MBZ alters the transcriptome of the AGP-01 lineage, mainly modulating the expression of histone proteins and inflammatory cytokines, indicating a possible epigenetic and immunological effect on tumor cells, these findings highlight new mechanisms of action related to MBZ treatment. Additional studies are still needed to better clarify the epigenetic and immune mechanism of MBZ in the therapy of GC.

PMID:39566281 | DOI:10.1016/j.compbiomed.2024.109415

Parasitol Res. 2024 Nov 20;123(11):387. doi: 10.1007/s00436-024-08389-6.

ABSTRACT

Acanthamoeba castellanii is the causative pathogen of a severe eye infection, known as Acanthamoeba keratitis and a life-threatening brain infection, named granulomatous amoebic encephalitis. Current treatments are problematic and costly and exhibit limited efficacy against Acanthamoeba parasite, especially the cyst stage. In parallel to drug discovery and drug repurposing efforts, drug modification is also an important approach to tackle infections, especially against neglected parasites such as free-living amoebae: Acanthamoeba. In this study, we determined whether modifying pentamidine and doxycycline through chitosan-functionalized graphene oxide loading enhances their anti-amoebic effects. Various concentrations of doxycycline, pentamidine, graphene oxide, chitosan-functionalized graphene oxide, and chitosan-functionalized graphene oxide loaded with doxycycline and pentamidine were investigated for amoebicidal effects against pathogenic A. castellanii belonging to the T4 genotype. Lactate dehydrogenase assays were performed to determine toxic effects of these various drugs and nanoconjugates against human cells. The findings revealed that chitosan-functionalized graphene oxide loaded with doxycycline demonstrated potent amoebicidal effects. Nanomaterials significantly (p < 0.05) inhibited excystation and encystation of A. castellanii without exhibiting toxic effects against human cells in a concentration-dependent manner, as compared with other formulations. These results indicate that drug modifications coupled with nanotechnology may be a viable avenue in the rationale development of effective therapies against Acanthamoeba infections.

PMID:39565414 | DOI:10.1007/s00436-024-08389-6

ACS Med Chem Lett. 2024 Oct 2;15(11):1907-1917. doi: 10.1021/acsmedchemlett.4c00358. eCollection 2024 Nov 14.

ABSTRACT

Diabetes mellitus (DM) is a global health concern, and dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target. The study used three machine learning and deep learning models to predict potential DPP-4 inhibitors using a curated data set of 6,750 compounds. The models included support vector machine (SVM), random forest (RF), naive Bayes (NB), and multitask deep neural network (MTDNN). The MTDNN model demonstrated strong predictive performance, achieving 98.62% train accuracy and 98.42% test accuracy for predicting DPP-4 inhibitors and a correlation coefficient of 0.979 for training and 0.977 for the test data set, with low training and test errors while predicting corresponding IC50 values. The MTDNN model predicted potential inhibitors using an external data set of FDA-approved drugs, identifying 100 compounds. Among these, five compounds stood out with promising molecular docking and dynamic profiles, suggesting their potential as repurposed drugs for targeting DPP-4 and offering hope for the future of diabetes treatment.

PMID:39563823 | PMC:PMC11571088 | DOI:10.1021/acsmedchemlett.4c00358

Bioinformatics. 2024 Nov 19:btae692. doi: 10.1093/bioinformatics/btae692. Online ahead of print.

ABSTRACT

MOTIVATION: Drug repositioning, identifying novel indications for approved drugs, is a cost-effective strategy in drug discovery. Despite numerous proposed drug repositioning models, integrating network-based features, differential gene expression, and chemical structures for high-performance drug repositioning remains challenging.

RESULTS: We propose a comprehensive deep pre-training and fine-tuning framework for drug repositioning, termed DrugRepPT. Initially, we design a graph pre-training module employing model-augmented contrastive learning on a vast drug-disease heterogeneous graph to capture nuanced interactions and expression perturbations after intervention. Subsequently, we introduce a fine-tuning module leveraging a graph residual-like convolution network to elucidate intricate interactions between diseases and drugs. Moreover, a Bayesian multi-loss approach is introduced to balance the existence and effectiveness of drug treatment effectively. Extensive experiments showcase the efficacy of our framework, with DrugRepPT exhibiting remarkable performance improvements compared to SOTA baseline methods (Improvement 106.13% on Hit@1 and 54.45% on mean reciprocal rank). The reliability of predicted results is further validated through two case studies, ie, gastritis and fatty liver, via literature validation, network medicine analysis, and docking screening.

AVAILABILITY AND IMPLEMENTATION: The code and results are available at https://github.com/2020MEAI/DrugRepPT.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39563444 | DOI:10.1093/bioinformatics/btae692

BMC Bioinformatics. 2024 Nov 19;25(1):361. doi: 10.1186/s12859-024-05976-3.

ABSTRACT

BACKGROUND: Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction.

METHODS: Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction.

RESULTS: DrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN's learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing.

PMID:39563226 | DOI:10.1186/s12859-024-05976-3

Sci Signal. 2024 Nov 19;17(863):eado8303. doi: 10.1126/scisignal.ado8303. Epub 2024 Nov 19.

ABSTRACT

High-grade serous ovarian cancer (HGSOC) is one of the deadliest cancers for women, with a low survival rate, no early detection biomarkers, a high rate of recurrence, and few therapeutic options. Forskolin, an activator of cyclic AMP signaling, has several anticancer activities, including against HGSOC, but has limited use in vivo. Its water-soluble derivative, colforsin daropate, has the same mechanism of action as forskolin and is used to treat acute heart failure. Here, we investigated the potential of colforsin daropate as a treatment for HGSOC. We found that colforsin daropate induced cell cycle arrest and apoptosis in cultured HGSOC cells and spheroids but had negligible cytotoxicity in immortalized, nontumorigenic fallopian tube secretory cells and ovarian surface epithelial cells. Colforsin daropate also prevented HGSOC cells from invading ovarian surface epithelial cell layers in culture. In vivo, colforsin daropate reduced tumor growth, synergized with cisplatin (a standard chemotherapy in ovarian cancer care), and improved host survival in subcutaneous and intraperitoneal xenograft models. These antitumor effects of colforsin daropate were mediated in part by its reduction in the abundance and transcriptional activity of the oncoprotein c-MYC, which is often increased in HGSOC. Our findings demonstrate that colforsin daropate may be a promising therapeutic that could be combined with conventional therapies to treat HGSOC.

PMID:39561220 | DOI:10.1126/scisignal.ado8303

Mol Carcinog. 2024 Nov 19. doi: 10.1002/mc.23848. Online ahead of print.

ABSTRACT

Breast cancer remains a leading cause of cancer-related mortality among women, with current therapeutic approaches often limited by resistance and recurrence, especially in aggressive subtypes like triple-negative breast cancer. Drug repurposing has emerged as a promising strategy to address these challenges. In this study, we investigate the potential of Imatinib, a repurposed tyrosine kinase inhibitor, to inhibit epithelial-mesenchymal transition (EMT) in breast cancer cells by modulating the Notch signalling pathway. Our findings reveal that Imatinib treatment leads to a significant reduction in cancer cell stemness, invasiveness, and migration potential, alongside decreased colony-forming ability. EMT reversal was marked by a 2.71-fold increase in E-cadherin expression, with concurrent downregulation of mesenchymal markers, including Fibronectin (1.78-fold) and Slug (2.15-fold). Mechanistically, Imatinib was found to inhibit p300 acetyltransferase activity, resulting in reduced levels of H3K18Ac and H3K27Ac, which in turn led to the downregulation of key Notch pathway proteins such as HES1 (2.94-fold), AKT (2.08-fold), and p21 (1.88-fold). These results highlight the ability of Imatinib to suppress EMT through modulation of the Notch signalling pathway, offering a novel therapeutic avenue for breast cancer treatment. Overall, Imatinib demonstrates considerable potential for repurposing in breast cancer management by targeting critical oncogenic pathways involved in EMT and cancer progression.

PMID:39560382 | DOI:10.1002/mc.23848

Behav Brain Res. 2024 Nov 16:115343. doi: 10.1016/j.bbr.2024.115343. Online ahead of print.

ABSTRACT

Iron is a trace metal that takes part in the maintenance of body homeostasis by, for instance, aiding in energy production and immunity. A body of evidence now demonstrates that dysfunction in iron metabolism can have detrimental effects and is intricately associated with the development of neuropsychiatric disorders, including Major Depressive Disorder (MDD), anxiety, and schizophrenia. For instance, changes in serum and central nervous system (CNS) levels of iron and in proteins mediating iron metabolism have been documented in patients grappling with the aforementioned diseases. By contrast, targeting iron metabolism by using iron chelators, for instance, has proven to be effective in alleviating disease burden. Therefore, here we review the state-of-the-art regarding the role of iron metabolism and its dysfunction in the context of neuropsychiatric disorders. Furthermore, we discuss how targeting iron metabolism can be an effective therapeutic option to tackle this class of diseases. Finally, we discuss the mechanisms linking this dysfunction to behavioral changes in these disorders. Harnessing the knowledge of iron metabolism is not only key to the characterization of novel molecular targets and disease biomarkers but also crucial to drug repurposing and drug design.

PMID:39557130 | DOI:10.1016/j.bbr.2024.115343

Comput Biol Med. 2024 Nov 17;184:109419. doi: 10.1016/j.compbiomed.2024.109419. Online ahead of print.

ABSTRACT

BACKGROUND: Multiple studies have aimed to consolidate drug-related data and predict drug effects. However, most of these studies have focused on integrating diverse data through correlation rather than representing them based on the pharmacodynamic mechanism of action (MOA). It is thus crucial to obtain interpretability to validate prediction results. In this study, we propose a novel framework to construct knowledge graphs that represent pharmacodynamic MOA, predict drug effects, and derive conceivable mechanistic pathways.

METHODS AND RESULTS: We constructed an interpretable pharmacodynamic mechanism knowledge graph (IPM-KG) by integrating various existing databases and combining them with the approach of this study to automatically fill in the missing data. This yielded a knowledge graph comprising 1455 drugs and 2547 diseases. Additionally, a graph neural network (GNN)-based approach was used to predict therapeutic medication and indication, which outperformed previous approaches that relied on correlation-based knowledge graphs lacking pharmacodynamic MOA representations. Furthermore, we proposed and assessed a method to interpret pharmacodynamic MOA using gene perturbation data. This feasibility study demonstrated the successful derivation of an accurate mechanism in approximately 50 % of cases. Additionally, it facilitated the identification of candidate drugs, which are currently unapproved but exhibit potential for drug repositioning, and their mechanisms of action.

CONCLUSIONS: This framework not only enables the derivation of highly accurate "drug-indication" predictions but also offers a basic mechanistic understanding, thereby facilitating future drug repositioning efforts.

PMID:39556916 | DOI:10.1016/j.compbiomed.2024.109419

bioRxiv [Preprint]. 2024 Oct 29:2024.10.29.620882. doi: 10.1101/2024.10.29.620882.

ABSTRACT

Neuroinflammation is a key pathological hallmark of Alzheimer's disease (AD). Investigational and FDA approved drugs targeting inflammation already exist, thus drug repurposing for AD is a suitable approach. BT-11 is an investigational drug that reduces inflammation in the gut and improves cognitive function. BT-11 is orally active and binds to lanthionine synthetase C-like 2 (LANCL2), a glutathione-s-transferase, thus potentially reducing oxidative stress. We investigated the effects of BT-11 long-term treatment on the TgF344-AD rat model. BT-11 reduced hippocampal-dependent spatial memory deficits, Aβ plaque load and neuronal loss in males, and mitigated microglia numbers in females. BT-11 treatment led to hippocampal transcriptomic changes in signaling receptor, including G-protein coupled receptor pathways. We detected LANCL2 in hippocampal nuclear and cytoplasmic fractions with potential different post-translational modifications, suggesting distinct functions based on its subcellular localization. LANCL2 was present in oligodendrocytes, showing a role in oligodendrocyte function. To our knowledge, these last two findings have not been reported. Overall, our data suggest that targeting LANCL2 with BT-11 improves cognition and reduces AD-like pathology by potentially modulating G-protein signaling and oligodendrocyte function. Our studies contribute to the field of novel immunomodulatory AD therapeutics, and merit further research on the role of LANCL2 in this disease.

PMID:39553925 | PMC:PMC11565763 | DOI:10.1101/2024.10.29.620882

Front Psychiatry. 2024 Oct 31;15:1464550. doi: 10.3389/fpsyt.2024.1464550. eCollection 2024.

ABSTRACT

For over seven decades, dopamine receptor 2 (D2 receptor) antagonists remained the mainstay treatment for neuropsychiatric disorders. Although it is effective for treating hyperdopaminergic symptoms, it is often ineffective for treating negative and cognitive deficits. Trace amine-associated receptor 1 (TAAR1) is a novel, pharmacological target in the treatment of schizophrenia and other neuropsychiatric conditions. Several TAAR1 agonists are currently being developed and are in various stages of clinical and preclinical development. Previous efforts to identify TAAR1 agonists have been hampered by challenges in pharmacological characterisation, the absence of experimentally determined structures, and species-specific preferences in ligand binding and recognition. Further, poor insights into the functional selectivity of the receptor led to the characterisation of ligands with analogous signalling mechanisms. Such approaches limited the understanding of divergent receptor signalling and their potential clinical utility. Recent cryogenic electron microscopic (cryo-EM) structures of human and mouse TAAR1 (hTAAR1 and mTAAR1, respectively) in complex with agonists and G proteins have revealed detailed atomic insights into the binding pockets, binding interactions and binding modes of several agonists including endogenous trace amines (β-phenylethylamine, 3-Iodothyronamine), psychostimulants (amphetamine, methamphetamine), clinical compounds (ulotaront, ralmitaront) and repurposed drugs (fenoldopam). The in vitro screening of drug libraries has also led to the discovery of novel TAAR1 agonists (asenapine, guanabenz, guanfacine) which can be used in clinical trials or further developed to treat different neuropsychiatric conditions. Furthermore, an understanding of unappreciated signalling mechanisms (Gq, Gs/Gq) by TAAR1 agonists has come to light with the discovery of selective compounds to treat schizophrenia-like phenotypes. In this review, we discuss the emergence of structure-based approaches in the discovery of novel TAAR1 agonists through drug repurposing strategies and structure-guided designs. Additionally, we discuss the functional selectivity of TAAR1 signalling, which provides important clues for developing disorder-specific compounds.

PMID:39553890 | PMC:PMC11565220 | DOI:10.3389/fpsyt.2024.1464550

Addiction. 2024 Nov 17. doi: 10.1111/add.16715. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Amphetamine-type stimulants are the second-most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)-approved treatments for amphetamine-type stimulant use disorders (ATSUD). The aim of this study was to utilize a drug discovery framework that integrates artificial intelligence (AI)-based drug prediction, clinical corroboration and mechanism of action analysis to identify FDA-approved drugs that can be repurposed for treating ATSUD.

DESIGN AND SETTING: An AI-based knowledge graph model was first utilized to prioritize FDA-approved drugs in their potential efficacy for treating ATSUD. Among the top 10 ranked candidate drugs, ketamine represented a novel candidate with few studies examining its effects on ATSUD. We therefore conducted a retrospective cohort study to assess the association between ketamine and ATSUD remission using US electronic health record (EHR) data. Finally, we analyzed the potential mechanisms of action of ketamine in the context of ATSUD.

PARTICIPANTS AND MEASUREMENTS: ATSUD patients who received anesthesia (n = 3663) or were diagnosed with depression (n = 4328) between January 2019 and June 2022. The outcome measure was the diagnosis of ATSUD remission within one year of the drug prescription.

FINDINGS: Ketamine for anesthesia in ATSUD patients was associated with greater ATSUD remission compared with other anesthetics: hazard ratio (HR) = 1.58, 95% confidence interval (CI) = 1.15-2.17. Similar results were found for ATSUD patients with depression when comparing ketamine with antidepressants and bupropion/mirtazapine with HRs of 1.51 (95% CI = 1.14-2.01) and 1.68 (95% CI = 1.18-2.38), respectively. Functional analyses demonstrated that ketamine targets several ATSUD-associated pathways including neuroactive ligand-receptor interaction and amphetamine addiction.

CONCLUSIONS: There appears to be an association between clinician-prescribed ketamine and higher remission rates in patients with amphetamine-type stimulant use disorders.

PMID:39552271 | DOI:10.1111/add.16715

J Immunoassay Immunochem. 2024 Nov 17:1-25. doi: 10.1080/15321819.2024.2429538. Online ahead of print.

ABSTRACT

SARS-CoV-2, identified in Wuhan, China, in December 2019, is the third coronavirus responsible for a global epidemic, following SARS-CoV (2002) and MERS-CoV (2012). Given the recent emergence of COVID-19, comprehensive immunological data are still limited. The susceptibility and severity of SARS-CoV-2 infection are influenced by various host factors, including hormonal changes, genetic variations, inflammatory biomarkers, and behavioral attitudes. Identifying genetic factors contributing to infection severity may accelerate therapeutic development, including drug repurposing, natural extracts, and post-vaccine interventions (Initiative and Covid, 2021). This review discusses the human protein machinery involved in (a) SARS-CoV-2 host receptors, (b) the human immune response, and (c) the impact of demographic and genetic differences on individual risk for COVID-19. This review aims to clarify host factors implicated in SARS-CoV-2 susceptibility and progression, highlighting potential therapeutic targets and supportive treatment strategies.

PMID:39552098 | DOI:10.1080/15321819.2024.2429538

Neuropsychopharmacol Rep. 2024 Nov 17. doi: 10.1002/npr2.12498. Online ahead of print.

ABSTRACT

BACKGROUND: An innovative New South Wales government funded statewide Cannabis Medicines Advisory Service (CMAS) operated between January 2018 and June 2022. The service provided comprehensive patient-specific and evidence-based information to support health professionals in prescribing and patient care decisions. This study aimed to describe real-world data collected by CMAS.

METHODS: A sub-set of de-identified, patient-specific enquiries collected between January 2021 and June 2022 (n = 123/567; 21.7%) were analyzed using R version 4.2.1. Diagnosis, indication, and comorbidities were coded using Medical Dictionary for Regulatory Activities (MedDRA) terminology.

RESULTS: Most patient-specific enquiries from medical practitioners were from general practitioners (n = 103/123; 83.7%). Female (n = 53/123; 43.1%) and male (n = 59/123; 48.0%) patients were similarly represented. Sex was not specified for 8.9% (n = 11/123) of patients. The mean age of patients was 52.1 years (range <10-90). The most common three diagnoses were osteoarthritis, anxiety, and chronic pain. Indications that were most frequently reported included chronic pain, anxiety, back pain, non-neuropathic pain, and insomnia. Comedications were most commonly non-opioid and opioid analgesics and antidepressants. Most practitioners were considering prescribing a cannabidiol (CBD) product for their patient. Cannabinoid composition selection guidance provided by CMAS was predominantly (delta-9-tetrahydrocannabinol) THC:CBD ~1:1, followed by CBD-only products. CMAS was contacted by health professionals regarding the management of potential adverse events for five patients.

CONCLUSION: The findings of this study shed light on the information medical practitioners were seeking to inform their clinical decision-making about medical cannabis and can inform the development of clinical guidance resources.

PMID:39551707 | DOI:10.1002/npr2.12498

Cancer Cell Int. 2024 Nov 16;24(1):381. doi: 10.1186/s12935-024-03573-1.

ABSTRACT

BACKGROUND: Despite advancements in therapeutic approaches, including taxane-based chemotherapy and androgen receptor-targeting agents, metastatic castration-resistant prostate cancer (mCRPC) remains an incurable tumor, highlighting the need for novel strategies that can target the complexities of this disease and bypass the development of drug resistance mechanisms. We previously demonstrated the synergistic antitumor interaction of valproic acid (VPA), an antiepileptic agent with histone deacetylase inhibitory activity, with the lipid-lowering drug simvastatin (SIM). This combination sensitizes mCRPC cells to docetaxel treatment both in vitro and in vivo by targeting the cancer stem cell compartment via mevalonate pathway/YAP axis modulation.

METHODS: Here, using a combined proteomic and metabolomic/lipidomic approach, we characterized tumor samples derived from 22Rv1 mCRPC cell-xenografted mice treated with or without VPA/SIM and performed an in-depth bioinformatics analysis.

RESULTS: We confirmed the specific impact of VPA/SIM on the Hippo-YAP signaling pathway, which is functionally related to the modulation of cancer-related extracellular matrix biology and metabolic reprogramming, providing further insights into the molecular mechanism of the antitumor effects of VPA/SIM.

CONCLUSIONS: In this study, we present an in-depth exploration of the potential to repurpose two generic, safe drugs for mCRPC treatment, valproic acid (VPA) and simvastatin (SIM), which already show antitumor efficacy in combination, primarily affecting the cancer stem cell compartment via MVP/YAP axis modulation. Bioinformatics analysis of the LC‒MS/MS and 1H‒NMR metabolomics/lipidomics results confirmed the specific impact of VPA/SIM on Hippo-YAP.

PMID:39550583 | DOI:10.1186/s12935-024-03573-1

BMC Cancer. 2024 Nov 16;24(1):1415. doi: 10.1186/s12885-024-13186-6.

ABSTRACT

BACKGROUND: Cancer cells alter their metabolic phenotypes with nutritional change. Single agent approaches targeting mitochondrial metabolism in cancer have failed due to either dose limiting off target toxicities, or lack of significant efficacy in vivo. To mitigate these clinical challenges, we investigated the potential utility of repurposing FDA approved mitochondrial targeting anthelmintic agents, niclosamide, IMD-0354 and pyrvinium pamoate, to be combined with GLUT1 inhibitor BAY-876 to enhance the inhibitory capacity of the major metabolic phenotypes exhibited by tumors.

METHODS: To test this, we used breast cancer cell lines MDA-MB-231 and 4T1 which exhibit differing basal metabolic rates of glycolysis and mitochondrial respiration, respectively. Metabolic characterization was carried out using Seahorse XFe96 Bioanalyzer and statistical analysis was carried out via ANOVA.

RESULTS: Here, we found that specific responses to mitochondrial and glycolysis targeting agents elicit responses that correlate with tested cell lines basal metabolic rates and fuel preference, highlighting the potential to cater metabolism targeting treatment regimens based on specific tumor nutrient handling. Inhibition of GLUT1 with BAY-876 potently inhibited glycolysis in both MDA-MB-231 and 4T1 cells, and niclosamide and pyrvinium pamoate perturbed mitochondrial respiration that resulted in potent compensatory glycolysis in the cell lines tested.

CONCLUSION: In this regard, combination of BAY-876 with both mitochondrial targeting agents resulted in inhibition of compensatory glycolysis and subsequent metabolic crisis. These studies highlight targeting tumor metabolism as a combination treatment regimen that can be tailored by basal and compensatory metabolic phenotypes.

PMID:39550554 | DOI:10.1186/s12885-024-13186-6