Comput Biol Chem. 2024 Dec 5;115:108302. doi: 10.1016/j.compbiolchem.2024.108302. Online ahead of print.

ABSTRACT

Drug Repurposing gives us facility to find the new uses of previously developed drugs rather than developing new drugs from start. Particularly during pandemic, drug repurposing caught much attention to provide new applications of the previously approved drugs. In our research, we provide a novel method for drug repurposing based on feature learning process from drug-disease-gene network. In our research, we aimed at finding drug candidates which can be repurposed under neurodegenerative diseases and glioma. We collected association data between drugs, diseases and genes from public resources and primarily examined the data related to Alzheimer's, Parkinson's and Glioma diseases. We created a Knowledge Graph using neo4j by integrating all these datasets and applied scalable feature learning algorithm known as node2vec to create node embeddings. These embeddings were later used to predict the unknown associations between disease and their candidate drugs by finding cosine similarity between disease and drug nodes embedding. We obtained a definitive set of candidate drugs for repurposing. These results were validated from the literature and CodReS online tool to rank the candidate drugs. Additionally, we verified the status of candidate drugs from pharmaceutical knowledge databases to confirm their significance.

PMID:39693851 | DOI:10.1016/j.compbiolchem.2024.108302

ACS Nano. 2024 Dec 18. doi: 10.1021/acsnano.4c10136. Online ahead of print.

ABSTRACT

The outbreak of emerging acute viral diseases urgently requires the acceleration of specialized antiviral drug development, thus widely adopting phenotypic screening as a strategy for drug repurposing in antiviral research. However, traditional phenotypic screening methods typically require several days of experimental cycles and lack visual confirmation of a drug's ability to inhibit viral infection. Here, we report a robust method that utilizes quantum-dot-based single-virus tracking and machine learning to generate unique single-virus infection fingerprint data from viral trajectories and detect the dynamic changes in viral movement following drug administration. Our findings demonstrated that this approach can successfully identify viral infection patterns at various infection phases and predict antiviral drug efficacy through machine learning within 90 min. This method provides valuable support for assessing the efficacy of antiviral drugs and offers promising applications for responding to future outbreaks of emerging viruses.

PMID:39692754 | DOI:10.1021/acsnano.4c10136

Brief Bioinform. 2024 Nov 22;26(1):bbae650. doi: 10.1093/bib/bbae650.

ABSTRACT

Drug repositioning, which involves identifying new therapeutic indications for approved drugs, is pivotal in accelerating drug discovery. Recently, to mitigate the effect of label sparsity on inferring potential drug-disease associations (DDAs), graph contrastive learning (GCL) has emerged as a promising paradigm to supplement high-quality self-supervised signals through designing auxiliary tasks, then transfer shareable knowledge to main task, i.e. DDA prediction. However, existing approaches still encounter two limitations. The first is how to generate augmented views for fully capturing higher-order interaction semantics. The second is the optimization imbalance issue between auxiliary and main tasks. In this paper, we propose a novel heterogeneous Graph Contrastive learning method with Gradient Balance for DDA prediction, namely GCGB. To handle the first challenge, a fusion view is introduced to integrate both semantic views (drug and disease similarity networks) and interaction view (heterogeneous biomedical network). Next, inter-view contrastive learning auxiliary tasks are designed to contrast the fusion view with semantic and interaction views, respectively. For the second challenge, we adaptively adjust the gradient of GCL auxiliary tasks from the perspective of gradient direction and magnitude for better guiding parameter update toward main task. Extensive experiments conducted on three benchmarks under 10-fold cross-validation demonstrate the model effectiveness.

PMID:39692448 | DOI:10.1093/bib/bbae650

MAbs. 2025 Dec;17(1):2440586. doi: 10.1080/19420862.2024.2440586. Epub 2024 Dec 17.

ABSTRACT

Antibody repurposing is the process of changing a known antibody so that it binds to a mutated antigen. One of the findings to emerge from the Coronavirus Disease 2019 (COVID-19) pandemic was that it was possible to repurpose neutralizing antibodies for Severe Acute Respiratory Syndrome, a related disease, to work for COVID-19. Thus, antibody repurposing is a possible pathway to prepare for and respond to future pandemics, as well as personalizing cancer therapies. For antibodies to be successfully repurposed, it is necessary to know both how antigen mutations disrupt their binding and how they should be mutated to recover binding, with this work describing an analysis to address the first of these topics. Every possible antigen point mutation in the interface of 246 antibody-protein complexes were analyzed using the Rosetta molecular mechanics force field. The results highlight a number of features of how antigen mutations affect antibody binding, including the effects of mutating critical hotspot residues versus other positions, how many mutations are necessary to be likely to disrupt binding, the prevalence of indirect effects of mutations on binding, and the relative importance of changing attractive versus repulsive energies. These data are expected to be useful in guiding future antibody repurposing experiments.

PMID:39690439 | DOI:10.1080/19420862.2024.2440586

Cell Rep. 2024 Dec 11:115061. doi: 10.1016/j.celrep.2024.115061. Online ahead of print.

ABSTRACT

The resistance of colorectal cancer liver metastases (CRLMs) to 5-fluorouracil (5-FU) chemotherapy remains a significant global health challenge. We investigated the phosphoproteomic dynamics of serial tissue sections obtained from initial metastases and recurrent tumors collected from 24 patients to address this unmet need for innovative therapeutic strategies for patients with CRLM with a poor prognosis. Our analysis revealed the activation of PAK kinase in patients with CRLM with a poor prognosis. Using an unbiased computational approach, we conducted a correlation analysis between PAK1 kinase activity and 545 drug sensitivity profiles across 35 colorectal cancer cell lines and identified PI3K inhibitors as potential therapeutic candidates. The efficacy of the FDA-approved PI3K inhibitor copanlisib was validated in 5-FU-resistant cell lines with high PAK1 kinase activity both in vitro and in vivo. This study presents an effective strategy for drug target discovery based on kinase activity, and the concept of this approach is widely applicable.

PMID:39689713 | DOI:10.1016/j.celrep.2024.115061

Leuk Lymphoma. 2024 Dec 17:1-19. doi: 10.1080/10428194.2024.2438800. Online ahead of print.

ABSTRACT

Hairy cell leukemia (HCL) is an indolent malignancy of mature B-lymphocytes. While existing front-line therapies achieve excellent initial results, a significant number of patients relapse and become increasingly treatment resistant. A major molecular driver of HCL is aberrant interlocking expression of the transcription factor JunD and the intracellular signaling molecule RhoH. Here we discuss the molecular basis of how the JunD-RhoH axis contributes to HCL pathogenesis. We also discuss how leveraging the JunD-RhoH axis identifies CD23, CD38, CD66a, CD115, CD269, integrin β7, and MET as new potential therapeutic targets. Critically, preclinical studies have already demonstrated that targeting CD38 with isatuximab effectively treats preexisiting HCL. Isatuximab and therapeutics directed against each of the other six new HCL targets are currently in clinical use to treat other disorders. Consequently, leveraging the JunD-RhoH axis has identified a battery of therapies that could be repurposed as new means of treating relapsed or refractory HCL.

PMID:39689307 | DOI:10.1080/10428194.2024.2438800

J Chem Inf Model. 2024 Dec 17. doi: 10.1021/acs.jcim.4c01966. Online ahead of print.

ABSTRACT

Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, more than 90% of RDs lack effective therapies. Traditional drug discovery models, marked by lengthy development cycles and high failure rates, struggle to meet the unique demands of RDs, often yielding poor returns on investment. However, the advent of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers groundbreaking solutions. This review explores AI's potential to revolutionize drug discovery for RDs by overcoming these challenges. It discusses AI-driven advancements, such as drug repurposing, biomarker discovery, personalized medicine, genetics, clinical trial optimization, corporate innovations, and novel drug target identification. By synthesizing current knowledge and recent breakthroughs, this review provides crucial insights into how AI can accelerate therapeutic development for RDs, ultimately improving patient outcomes. This comprehensive analysis fills a critical gap in the literature, enhancing understanding of AI's pivotal role in transforming RD research and guiding future research and development efforts in this vital area of medicine.

PMID:39689164 | DOI:10.1021/acs.jcim.4c01966

J Biomol Struct Dyn. 2024 Dec 17:1-15. doi: 10.1080/07391102.2024.2438361. Online ahead of print.

ABSTRACT

Drug repurposing is preferred over de-novo drug discovery to unveil the therapeutic applications of existing drug candidates before investing considerable resources in unexplored novel chemical entities. This study demonstrated multifaceted stratagems to reconnoiter promising repurposable candidates against Hepatocellular Carcinoma (HCC) by amalgamating Real-World-Data (RWD) with bioinformatics algorithms corroborated with in-silico and in-vitro studies. At the outset, the RWD from the Food and Drug Administration Adverse Event Reporting System (FAERS) was explored to navigate signals to retrieve repurposable drugs that are inversely associated with HCC via Disproportionality Analysis. Further, transcriptomic analysis was used to capture the potential targets of HCC. Following this, the interactions between repurposable drugs and HCC targets were virtually demonstrated via molecular docking and Molecular Dynamics Simulations (MDS). Furthermore, additional cytotoxicity and gene expression experiments were conducted to corroborate the results. Overall, 64 drugs with Drug Event >5 were shortlisted as prospective repurposable drugs as per the RWD obtained from FAERS. The transcriptomic analysis highlighted significant upregulation of Cyclin A2 (CCNA2) in HCC, which activates Cyclin Dependent Kinase 2 (CDK2). Further, in-silico studies identified Losartan and Allopurinol, with docking scores of -7.11 and -6.219, respectively, as potential repurposable drugs. The selected drugs underwent further scrutiny through in-vitro studies. The treatment of HepG2 cells with Allopurinol resulted in significant downregulation of CCNA2/CDK2 expression with an elevation in reactive oxygen species levels, uncovering Allopurinol's anticancer mechanism through cellular apoptosis. This study suggests the importance of RWD in drug repurposing and the potential of Allopurinol as a repurposable drug against HCC.

PMID:39687947 | DOI:10.1080/07391102.2024.2438361

J Cachexia Sarcopenia Muscle. 2024 Dec 17. doi: 10.1002/jcsm.13665. Online ahead of print.

ABSTRACT

BACKGROUND: Muscle atrophy, including glucocorticoid-induced muscle wasting from treatments such as dexamethasone (DEX), results in significant reductions in muscle mass, strength and function. This study investigates the potential of lonafarnib, a farnesyltransferase inhibitor, to counteract DEX-induced muscle atrophy by targeting key signalling pathways.

METHODS: We utilized in vitro models with C2C12 myotubes treated with DEX and in vivo models with Caenorhabditis elegans and DEX-treated Sprague-Dawley rats. Myotube morphology was assessed by measuring area, fusion index and diameter. Muscle function was evaluated by grip strength and compound muscle action potential (CMAP) in the gastrocnemius (GC) and tibialis anterior (TA) muscles. Molecular mechanisms were explored through RNA sequencing and Western blotting to assess changes in mitochondrial function and muscle signalling pathways.

RESULTS: Lonafarnib (2 μM) significantly improved myotube area (1.49 ± 0.14 × 105 μm2 vs. 1.03 ± 0.49 × 105 μm2 in DEX, p < 0.05), fusion index (18.73 ± 1.23% vs. 13.3 ± 1.56% in DEX, p < 0.05) and myotube diameter (31.89 ± 0.89 μm vs. 21.56 ± 1.01 μm in DEX, p < 0.05) in C2C12 myotubes. In C. elegans, lonafarnib (100 μM) increased the pharyngeal pumping rate from 212 ± 7.21 contractions/min in controls to 308 ± 17.09 contractions/min at day 4 (p < 0.05), indicating enhanced neuromuscular function. In DEX-induced atrophic rats, lonafarnib improved maximal grip strength (DEX: 13.91 ± 0.78 N vs. 1 μM lonafarnib: 16.18 ± 0.84 N and 5 μM lonafarnib: 16.71 ± 0.83 N, p < 0.05), increased muscle weight in GC, and enhanced CMAP amplitudes in both GC and TA muscles. Western blot analysis showed that lonafarnib treatment upregulated UCP3 and ANGPTL4 and increased phosphorylation of mTOR and S6 ribosomal protein (p < 0.05), indicating enhanced mitochondrial function and protein synthesis. Knockdown models further demonstrated that lonafarnib could partially rescue muscle atrophy phenotypes, indicating its action through multiple molecular pathways.

CONCLUSIONS: Lonafarnib mitigates dexamethasone-induced muscle atrophy by enhancing mitochondrial function and activating anabolic pathways. These findings support further investigation of lonafarnib as a therapeutic agent for muscle atrophy in clinical settings.

PMID:39686867 | DOI:10.1002/jcsm.13665

Cardiovasc Hematol Disord Drug Targets. 2024 Dec 16. doi: 10.2174/011871529X341930241206063315. Online ahead of print.

ABSTRACT

BACKGROUND: Drug repurposing involves investigating new indications or uses for drugs that have already been approved for clinical use. Empagliflozin is a C-glycosyl compound characterized by the presence of a beta-glucosyl residue. It functions as a sodium-glucose co-transporter 2 inhibitor and is utilized to enhance glycemic control in adults diagnosed with type 2 diabetes mellitus. Additionally, it is indicated for the reduction of cardiovascular mortality risk in adult patients who have both type 2 diabetes mellitus and pre-existing cardiovascular disease.

OBJECTIVE: The study's objective revolves around exploring the repurposing potential of a novel SGLT2 inhibitor acting as an antidiabetic drug named Empagliflozin through computational methods, with a specific focus on its interaction with cardioprotective key target proteins.

METHODS: The study was performed by docking the empagliflozin with different target proteins (NHE1- CHP1, BIRC5, GLUT1, and XIAP) by using Autodock, and different values were recorded. The docked files were analysed by the BIOVIA Discovery Studio Visualizer. The in silico analysis conducted in this study examines the binding free energy values of Empagliflozin with key target proteins.

RESULTS: Results revealed that NHE1-CHP1 exhibits the lowest binding free energy, followed by BIRC5, GLUT1, and XIAP, with the highest value. This descending order of binding energies suggests varying degrees of effectiveness in binding molecules, with lower energies indicative of more potent biological activity. The analysis underscores the importance of intermolecular interactions, particularly hydrogen bond formations facilitated by oxygen, nitrogen, and carbonyl groups in compound structures. Notably, NHE1-CHP1 demonstrates superior binding interactions with Empagliflozin compared to the other target proteins, highlighting its potential as a cardioprotective agent.

CONCLUSION: These findings offer valuable insights into the therapeutic possibilities of Empagliflozin in cardioprotection, indicating promising avenues for further research and development in this domain.

PMID:39686639 | DOI:10.2174/011871529X341930241206063315

Pharmacol Res Perspect. 2025 Feb;13(1):e70049. doi: 10.1002/prp2.70049.

ABSTRACT

Medicine repurposing is a strategy to identify new uses for the existing medicines for the purpose of addressing areas of unmet medical need. This paper aims to provide horizon scanning intelligence on repurposed medicines that are evaluated by non-commercial organizations such as academia and highlights opportunities for further research to improve patient health outcomes. A scan of the clinical landscape of non-commercially sponsored repurposed medicines is routinely conducted by the NIHR Innovation Observatory (IO). This ongoing project involves a horizon scan of clinical trial registries and the IO's internal horizon scanning Medicines Innovation Database to identify potential candidate medicines used as monotherapy or in combination to treat new indications outside the scope of their licensed indication. In addition to making these data publicly available, the output also supports the NHS England Medicines Repurposing Programme. The snapshot scan reported here (trials completing April 2020-March 2023) identified a total of 528 technologies (meaning, a single product or combination of medicinal products targeting a specific indication in one or more related trials). The technologies were classified according to their characteristics and targeted therapeutic indications as well as revealing the least treated disease conditions. The candidate medicines identified in this scan could potentially receive tailored support toward adoption into practice and policy. The NIHR IO regularly provides this scan as a source of intelligence on repurposed medicines. This provides valuable insights into innovation trends, gaps, and areas of unmet clinical need.

PMID:39686549 | DOI:10.1002/prp2.70049

Int J Mol Sci. 2024 Nov 25;25(23):12649. doi: 10.3390/ijms252312649.

ABSTRACT

Cardiovascular diseases (CVD) pose a significant global health challenge, requiring innovative therapeutic strategies. Vasodilators, which are central to vasodilation and blood pressure reduction, play a crucial role in cardiovascular treatment. This study integrates quantitative structure- (QSAR) modeling and molecular dynamics (MD) simulations to predict the biological activity and interactions of vasodilatory compounds with the aim to repurpose drugs already known and estimateing their potential use as vasodilators. By exploring molecular descriptors, such as electronegativity, softness, and highest occupied molecular orbital (HOMO) energy, this study identifies key structural features influencing vasodilatory effects, as it seems molecules with the same mechanism of actions present similar frontier orbitals pattern. The QSAR model was built using fifty-four Food Drugs Administration-approved (FDA-approved) compounds used in cardiovascular treatment and their activities in rat thoracic aortic rings; several molecular descriptors, such as electronic, thermodynamics, and topographic were used. The best QSAR model was validated through robust training and test dataset split, demonstrating high predictive accuracy in drug design. The validated model was applied on the FDA dataset and molecules in the application domain with high predicted activity were retrieved and filtered. Thirty molecules with the best-predicted pKI50 were further analyzed employing molecular orbital frontiers and classified as angiotensin-I or β1-adrenergic inhibitors; then, the best scoring values obtained from molecular docking were used to perform a molecular dynamics simulation, providing insight into the dynamic interactions between vasodilatory compounds and their targets, elucidating the strength and stability of these interactions over time. According to the binding energies results, this study identifies novel vasodilatory candidates where Dasabuvir and Sertindole seem to have potent and selective activity, offering promising avenues for the development of next-generation cardiovascular therapies. Finally, this research bridges computational modelling with experimental validation, providing valuable insight for the design of optimized vasodilatory agents to address critical unmet needs in cardiovascular medicine.

PMID:39684360 | DOI:10.3390/ijms252312649

Molecules. 2024 Nov 25;29(23):5564. doi: 10.3390/molecules29235564.

ABSTRACT

Amidst the ongoing global challenge of the SARS-CoV-2 pandemic, the quest for effective antiviral medications remains paramount. This comprehensive review delves into the dynamic landscape of FDA-approved medications repurposed for COVID-19, categorized as antiviral and non-antiviral agents. Our focus extends beyond conventional narratives, encompassing vaccination targets, repurposing efficacy, clinical studies, innovative treatment modalities, and future outlooks. Unveiling the genomic intricacies of SARS-CoV-2 variants, including the WHO-designated Omicron variant, we explore diverse antiviral categories such as fusion inhibitors, protease inhibitors, transcription inhibitors, neuraminidase inhibitors, nucleoside reverse transcriptase, and non-antiviral interventions like importin α/β1-mediated nuclear import inhibitors, neutralizing antibodies, and convalescent plasma. Notably, Molnupiravir emerges as a pivotal player, now licensed in the UK. This review offers a fresh perspective on the historical evolution of COVID-19 therapeutics, from repurposing endeavors to the latest developments in oral anti-SARS-CoV-2 treatments, ushering in a new era of hope in the battle against the pandemic.

PMID:39683724 | DOI:10.3390/molecules29235564

Nutrients. 2024 Nov 28;16(23):4109. doi: 10.3390/nu16234109.

ABSTRACT

BACKGROUND: Oncostatin M (OSM) is involved in several inflammatory responses. Tryptanthrin (TRYP), as a natural alkaloid, is a bioactive compound derived from indigo plants. Objectives/ Methods: The purpose of this study is to investigate the potential inhibitory activity of TRYP on OSM release from neutrophils using neutrophils-like differentiated (d)HL-60 cells and neutrophils from mouse bone marrow.

RESULTS: The results showed that TRYP reduced the production and mRNA expression levels of OSM in the granulocyte-macrophage colony-stimulating factor (GM-CSF)-stimulated neutrophils-like dHL-60 cells. In addition, TRYP decreased the OSM production levels in the GM-CSF-stimulated neutrophils from mouse bone marrow. TRYP inhibited the phosphorylation of phosphatidylinositol 3-kinase (PI3K), AKT, and nuclear factor (NF)-κB in the GM-CSF-stimulated neutrophils-like dHL-60 cells.

CONCLUSIONS: Therefore, these results reveal for the first time that TRYP inhibits OSM release via the down-regulation of PI3K-AKT-NF-κB axis from neutrophils, presenting its potential as a therapeutic agent for inflammatory responses.

PMID:39683503 | DOI:10.3390/nu16234109

Cancers (Basel). 2024 Nov 29;16(23):4003. doi: 10.3390/cancers16234003.

ABSTRACT

BACKGROUND/OBJECTIVES: Perillyl alcohol (POH), a plant-derived compound, has demonstrated anti-tumor activity across various human cancers. Understanding the regulatory pathways through which POH exerts its effects is crucial for identifying new therapeutic opportunities and exploring potential drug repositioning strategies. Therefore, this scoping review aims to provide a comprehensive overview of the metabolic and regulatory pathways involved in the anticancer effects of POH, based on in vitro evidence.

METHODS: Following the PRISMA-ScR 2018 guidelines, a systematic search was conducted in the PUBMED, Web of Science, and Scopus databases.

RESULTS: A total of 39 studies were included, revealing that POH exerts its biological effects by modulating several pathways, including the regulation of cyclins, CDKs, and p21, thereby affecting cell cycle progression. It inhibits growth and promotes cell death by attenuating AKT phosphorylation, reducing PARP-1 activity, increasing caspase activity and the FAS receptor and its ligand FASL. Additionally, POH reduces ERK phosphorylation, inhibits RAS protein isoprenylation, and decreases Na/K-ATPase activity.

CONCLUSIONS: In conclusion, this review delineates the key regulatory pathways responsible for mediating the biological effects of POH in cancer.

PMID:39682189 | DOI:10.3390/cancers16234003

Cancers (Basel). 2024 Nov 27;16(23):3980. doi: 10.3390/cancers16233980.

ABSTRACT

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer with limited treatment options. The nuclear export protein XPO1 has emerged as a potential therapeutic target in cancer, but its role in TNBC has not been fully characterized. This study investigates the potential of repurposing selinexor, an FDA-approved XPO1 inhibitor, as a novel therapeutic options for TNBC. Methods: A computational drug repurposing pipeline was used to predict patient tumor responses to hundreds of drugs. We identified XPO1 inhibitors as a candidate drug and validated its efficacy on an independent patient dataset and across various TNBC cell lines. RNA-sequencing after longitudinal XPO1 inhibition and further mechanistic studies were performed to explore and confirm the leading causes of TNBC cell sensitivity to XPO1 inhibition. Results: Selinexor significantly reduce the viability of a variety of TNBC cell lines. Mechanistically, selinexor induces TNBC cell death by inhibiting the NF-kB pathway through nuclear retention of NFKBIA. This effect was consistent across multiple TNBC cell lines. Conclusions: XPO1 inhibitors show promise as targeted therapies for TNBC patients. New mechanistic insight into the causes leading to TNBC sensitivity to XPO1-inhibition-mediated cell death warrant further clinical trials to evaluate the safety and efficacy in TNBC.

PMID:39682167 | DOI:10.3390/cancers16233980

Sci Rep. 2024 Dec 16;14(1):30488. doi: 10.1038/s41598-024-81414-x.

ABSTRACT

In this study, we investigated a novel anti-cancer drug design approach by revisiting diclofenac-based carborane-substituted prodrugs. The redesigned compounds combine the robust carborane scaffold with the oxindole framework, resulting in four carborane-derivatized oxindoles and a unique zwitterionic amidine featuring a nido-cluster. We tested the anti-cancer potential of these prodrugs against murine colon adenocarcinoma (MC38), human colorectal carcinoma (HCT116), and human colorectal adenocarcinoma (HT29). The tests showed that diclofenac and the carborane-substituted oxindoles exhibited no cytotoxicity, the dichlorophenyl-substituted oxindole had moderate anti-cancer activity, while with the amidine this effect was strongly potentiated with activity mapping within low micromolar range. Compound 3 abolished the viability of selected colon cancer cell line MC38 preferentially through strong inhibition of cell division and moderate apoptosis accompanied by ROS/RNS depletion. Our findings suggest that carborane-based prodrugs could be a promising direction for new anti-cancer therapies. Inhibition assays for COX-1 and COX-2 revealed that while diclofenac had strong COX inhibition, the re-engineered carborane compounds demonstrated a varied range of anti-cancer effects, probably owing to both, COX inhibition and COX-independent pathways.

PMID:39681576 | DOI:10.1038/s41598-024-81414-x

Arthritis Rheumatol. 2024 Dec 16. doi: 10.1002/art.43088. Online ahead of print.

ABSTRACT

OBJECTIVE: Idiopathic inflammatory myopathies (myositis, IIMs) are rare, systemic autoimmune disorders that lead to muscle inflammation, weakness, and extra-muscular manifestations, with a strong genetic component influencing disease development and progression. Previous genome-wide association studies identified loci associated with IIMs. In this study, we imputed data from two prior genome-wide myositis studies and analyzed the largest myositis dataset to date to identify novel risk loci and susceptibility genes associated with IIMs and its clinical subtypes.

METHODS: We performed association analyses on 14,903 individuals (3,206 cases and 11,697 controls) with genotypes and imputed data from the Trans-Omics for Precision Medicine (TOPMed) reference panel. Fine-mapping and expression quantitative trait locus co-localization analyses in myositis-relevant tissues indicated potential causal variants. Functional annotation and network analyses using the random walk with restart (RWR) algorithm explored underlying genetic networks and drug repurposing opportunities.

RESULTS: Our analyses identified novel risk loci and susceptibility genes, such as FCRLA, NFKB1, IRF4, DCAKD, and ATXN2 in overall IIMs; NEMP2 in polymyositis; ACBC11 in dermatomyositis; and PSD3 in myositis with anti-histidyl-tRNA synthetase autoantibodies (anti-Jo1). We also characterized effects of HLA region variants and the role of C4. Colocalization analyses suggested putative causal variants in DCAKD in skin and muscle, HCP5 in lung, and IRF4 in EBV-transformed lymphocytes, lung, and whole blood. RWR further prioritized additional candidate genes, including APP, CD74, CIITA, NR1H4, and TXNIP, for future investigation.

CONCLUSION: Our study uncovers novel genetic regions contributing to IIMs, advancing our understanding of myositis pathogenesis and offering new insights for future research.

PMID:39679859 | DOI:10.1002/art.43088

medRxiv [Preprint]. 2024 Dec 2:2024.12.01.24318008. doi: 10.1101/2024.12.01.24318008.

ABSTRACT

Neuropsychiatric disorders lack effective treatments due to a limited understanding of underlying cellular and molecular mechanisms. To address this, we integrated population-scale single-cell genomics data and analyzed cell-type-level gene regulatory networks across schizophrenia, bipolar disorder, and autism (23 cell classes/subclasses). Our analysis revealed potential druggable transcription factors co-regulating known risk genes that converge into cell-type-specific co-regulated modules. We applied graph neural networks on those modules to prioritize novel risk genes and leveraged them in a network-based drug repurposing framework to identify 220 drug molecules with the potential for targeting specific cell types. We found evidence for 37 of these drugs in reversing disorder-associated transcriptional phenotypes. Additionally, we discovered 335 drug-associated cell-type eQTLs, revealing genetic variation's influence on drug target expression at the cell-type level. Our results provide a single-cell network medicine resource that provides mechanistic insights for advancing treatment options for neuropsychiatric disorders.

PMID:39677458 | PMC:PMC11643187 | DOI:10.1101/2024.12.01.24318008

J Assoc Physicians India. 2024 Dec;72(12):49-54. doi: 10.59556/japi.72.0755.

ABSTRACT

The prevalence of Alzheimer's disease (AD) is rising with an aging population worldwide and is expected to surpass 130 million by 2050. India is no exception, but the true prevalence data on AD is not conclusive. By 2050, India will have almost 15% of the population aged 60 years or above. It is the need of the hour to have newer and more effective agents that can address various therapeutic needs of Alzheimer's viz., halt or delay disease progression, and offer better improvement in symptomatology. The most desirable would be to have an intervention that can prevent AD onset. The prime focus of the present review is to introduce to the readers the promising drug candidates across the world. We reviewed all the information available to us through a literature search. It is quite apparent that the developmental efforts are concentrated not only on disease-modifying therapies that can prevent the development but also on palliative therapies that improve the quality of life of AD patients. Several approaches including biological and small molecules are being explored to tap their potential in AD therapeutics using sound scientific research principles and execution. Besides conventional development approaches, the drug repurposing strategy has emerged as quick, cost-effective, and less risky and is being exploited to the fullest. The drugs in the pipeline and undergoing various phases of clinical trials for the past 5 years are taken from the ClinicalTrials.gov registry. It remains to be seen the advent of a successful disease-modifying agent for AD in future.

PMID:39676195 | DOI:10.59556/japi.72.0755