J Appl Microbiol. 2025 Jan 9:lxaf012. doi: 10.1093/jambio/lxaf012. Online ahead of print.

ABSTRACT

AIMS: To investigate the effects of simvastatin as an antimicrobial, considering its influence on the mevalonate pathway and on the bacterial cell wall of Staphylococcus aureus.

METHODS AND RESULTS: S. aureus ATCC 29213 and 33591 were exposed to simvastatin in the presence of exogenous mevalonate to determine whether mevalonate could reverse the inhibition. S. aureus was also treated with simvastatin and gene expression analysis assays were performed to evaluate genes associated with the mevalonate pathway (mvaA, mvaS, mvaK1, and mvaK2), peptidoglycan synthesis (uppS, uppP, and murG), and cell wall stress (vraX, sgtB, and tcaA). Transmission electron microscopy was used to identify the presence of morphological changes. The data were compared using two-way ANOVA and Bonferroni post-test, or the Mann-Whitney test. Addition of exogenous mevalonate was able to partially or completely reverse the inhibition caused by simvastatin. A significant increase of the vraX gene and a reduction of the mvaA gene were observed, together with changes in bacterial morphology.

CONCLUSION: Simvastatin can exert its antimicrobial effect by means of changes in the cell wall associated with the mevalonate pathway.

PMID:39788721 | DOI:10.1093/jambio/lxaf012

Int J Biol Macromol. 2025 Jan 7:139590. doi: 10.1016/j.ijbiomac.2025.139590. Online ahead of print.

ABSTRACT

SARS-CoV-2 encodes a 3C-like protease (3CLpro) that is essential for viral replication. This cysteine protease cleaves viral polyproteins to release functional nonstructural proteins, making it a prime target for antiviral drug development. We investigated the inhibitory effects of halicin, a known c-Jun N-terminal kinase inhibitor, on 3CLpro. Mass spectrometry and crystallographic analysis revealed that halicin covalently binds to several cysteine residues in 3CLpro. As expected, Cys145, the catalytic residue, was found to be the most targeted residue by halicin. Secondly, Cys44 was found to be modified, suggesting a potential inhibitory role of this residue. A mutant protease (Cys44Ala) was generated to further understand the function of Cys44. In silico and enzymatic assays showed that the mutation significantly reduced the stability and activity of 3CLpro, indicating the importance of Cys44 in maintaining the active conformation of the protease. Differential scanning fluorimetry assays confirmed this evidence, showing a reduced thermal stability of the mutant compared to the wild-type protease. Our results highlight the potential of halicin as a multi-target inhibitor of 3CLpro and underline the importance of Cys44 in the function of the protease. These findings contribute to the development of effective antiviral therapies against COVID-19 by targeting critical residues in 3CLpro.

PMID:39788258 | DOI:10.1016/j.ijbiomac.2025.139590

PLoS One. 2025 Jan 9;20(1):e0314151. doi: 10.1371/journal.pone.0314151. eCollection 2025.

ABSTRACT

Aquaporin 1 (AQP1) is a key channel for water transport in peritoneal dialysis. Inhibition of AQP1 could therefore impair water transport during peritoneal dialysis. It is not known whether inhibition of AQP1 occurs unintentionally due to off-target interactions of administered medications. A high-throughput virtual screening study has been performed to investigate the possible binding of licensed medications to the water pore of human AQP1. A complete model of human AQP1 based on its canonical sequence was assembled using I-TASSER and MODELLER. The model was refined via the incorporation of pore water molecules from a high-resolution yeast aquaporin structure. Docking studies were conducted for the cytoplasmic domain of the AQP1 monomer against a library of all compounds listed in the British National Formulary (BNF), using the PLANTS software with the ChemPLP scoring function. The stability of the best docked conformations within the intrinsic water pore was assessed via short 15 nanosecond molecular dynamics (MD) simulations using the GROMACS-on-Colab utility. Of the 1512 compounds tested, 1002 docking results were obtained, and 198 of these conformations occupied a position within the intrinsic water pore. 30 compounds with promising docking scores were assessed by MD. The docked conformations for dopamine, gabapentin, pregabalin, and methyldopa were stable in these short MD studies. For furosemide and pravastatin, the MD trajectory suggested a binding mode different to the docking result. A small set of compounds which could impede water transport through human AQP1 have been identified in this computational screening study.

PMID:39787482 | DOI:10.1371/journal.pone.0314151

Alzheimers Dement. 2024 Dec;20 Suppl 2:e086490. doi: 10.1002/alz.086490.

ABSTRACT

BACKGROUND: Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Its underlying mechanisms remain elusive and specific mechanism-based drugs are lacking.

METHOD: We integrated more than 2,800 CSF and 4,600 plasma pQTL, derived from the largest proteomic studies so far (SOMAscan 7k and 4k; in up to 35,559 individuals), and the two most prevalent MRI-markers of cSVD (MRI-cSVD, white matter hyperintensities and perivascular spaces burden; in up to 48,454 individuals) in a Mendelian Randomization (MR) framework to identify causal and druggable targets for cSVD. Identified association were followed-up using a multipronged approach: across fluids, proteomics platforms (Olink 3072, N=8,590) and lifespan (N=1,748), using both MR and individual-level data.

RESULT: We found 51 proteins associated with MRI-cSVD of which 46 in CSF and 9 in plasma. Among available significant CSF- and plasma-proteins, 32% and 31% replicated in cross-fluid and cross-platform follow-up, and 47% were associated with stroke and/or dementia at least at nominal significance. We found converging evidence that protein-cSVD associations are enriched in extracellular matrix and immune response pathways. Immunity-related proteins already showed association with MRI-cSVD already in young adults in their twenties. Furthermore, we provide genetic support for drug repositioning opportunities for cSVD, including compounds crossing the blood brain barrier.

CONCLUSION: Together, these findings provide a novel proteogenomic signature of cSVD and pave the way for novel therapeutic developments.

PMID:39785542 | DOI:10.1002/alz.086490

Alzheimers Dement. 2024 Dec;20 Suppl 7:e083393. doi: 10.1002/alz.083393.

ABSTRACT

Psychosis is a common and distressing disorder in people with Alzheimer disease, associated with a poor clinical prognosis, an increased risk of institutionalization and for which there are no approved treatments. New approaches to diagnosis and symptom assessment and treatment are beginning to move the field forward, including the emergence of psychosis at the pre-clinical or even pre-cognitive impairment stages of disease in some individuals. The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) research criteria for psychosis in neurodegenerative disease, and the ISTAART criteria for mild behavioural impairment are examples of recent developments. New genomic, neuroimaging, post-mortem and neurobiology studies are beginning to refine our mechanistic understanding and providing novel opportunities for drug discovery, drug repurposing and potentially for better approaches to precision medicine. Emerging potential therapies include the 5HT2A inverse agonist pimavanserin, which is already licensed in the US for the treatment of Parkinson's disease, escitalopram, muscarinic agonists and cannabidiol. Emerging data also highlight opportunities to optimize and develop more targeted psychological therapies for people with Alzheimer's disease psychosis. The treatment of psychosis also remains a major challenge in synuclein dementias where psychosis is more frequent and more persistent, and where many patients experience severe sensitivity reactions to antipsychotic medications. There is very little work examining the mechanisms or treatment of psychosis in people with vascular dementia, which remains a major unmet need. Although the assessment and management of psychosis in people with dementia remain challenging, improved diagnosis, evolving mechanistic understanding and an increased focus on new treatment studies are providing direction and new opportunities to the field and to people with Alzheimer's disease.

PMID:39784914 | DOI:10.1002/alz.083393

J Alzheimers Dis. 2025 Jan 9:13872877241305740. doi: 10.1177/13872877241305740. Online ahead of print.

ABSTRACT

Seizures in people with Alzheimer's disease are increasingly recognized to worsen disease burden and accelerate functional decline. Harnessing established antiseizure medicine discovery strategies in rodents with Alzheimer's disease associated risk genes represents a novel way to uncover disease modifying treatments that may benefit these Alzheimer's disease patients. This commentary discusses the recent evaluation by Dejakaisaya and colleagues to assess the antiseizure and disease-modifying potential of the repurposed cephalosporin antibiotic, ceftriaxone, in the Tg2576 mouse model. The use of established epilepsy models in Alzheimer's disease research carries the potential to advance novel disease-modifying treatments.

PMID:39784685 | DOI:10.1177/13872877241305740

Alzheimers Dement. 2024 Dec;20 Suppl 8:e095063. doi: 10.1002/alz.095063.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a devastating form of dementia, and its prevalence is rising as human lifespan increases. Our lab created the AD-BXD mouse model, which expresses AD mutations across a genetically diverse reference panel (BXD), to identify factors that confer resilience to cognitive decline in AD. This model mimics key characteristics of human AD including variation in age of onset and severity of cognitive decline.

METHOD: To facilitate discovery of conserved mechanisms of resilience to AD, we generated a cross-species single-nuclei transcriptomic dataset from normal and AD human (ROSMAP) and AD-BXD mouse frontal cortex tissue. We interrogated resilience-associated gene expression signatures, validated resilience candidate genes with human reference data, and used a druggability ranking and drug repositioning pipeline to nominate drugs to promote resilience to AD. To learn more about the context of resilience gene expression, we used a hierarchical mapping algorithm to predict anatomical locations of cells expressing resilience gene signatures.

RESULT: We found the strongest gene expression signature associated with cognitive resilience to AD arises from excitatory layer 4/5 (eL4/5) cortical intratelencephalic neurons. This resilience signature includes genes involved in synaptic plasticity, vesicle transport, and axonal and dendritic development. We found that 27 of the 61 genes in the resilience signature are druggable and identified several candidate drugs for further investigation (Telpoukhovskaia et al., 2022). We also identified genes expressed across a continuum of cognitive performance. Our hierarchical mapping approach showed that the eL4/5 neurons expressing resilience signature genes are distributed throughout the frontal cortex, mainly in the somatomotor area.

CONCLUSION: We identified 61 candidate resilience genes to target with new or existing drugs. We also determined that expression of resilience candidate genes occurs in eL4/5 neurons in the somatomotor region of the cortex. Ongoing projects in the lab aim to evaluate efficacy of nominated drugs and profile learning-specific proteomes of eL4/5 neurons in resilient and susceptible AD-BXD strains. When integrated with existing genetic, behavioral, and pathological data, our work will elucidate the cellular, molecular, and genetic mechanisms that contribute to cognitive resilience in face of neurodegenerative disease pathology.

PMID:39783534 | DOI:10.1002/alz.095063

Alzheimers Dement. 2024 Dec;20 Suppl 8:e095709. doi: 10.1002/alz.095709.

ABSTRACT

BACKGROUND: Cerebral amyloid angiopathy (CAA), the accumulation of amyloid proteins in the cerebral vasculature, increases the risk of stroke and vascular cognitive impairment and dementia (VCID). Not only is there no treatment for CAA, but the condition is also highly comorbid with Alzheimer's disease (AD), and its presence may serve as a contraindication to treating patients with anti-amyloid therapies due to an increased risk of hemorrhage and edema. Therefore, it is crucial to identify novel treatments for individuals with CAA. Epidemiological studies suggest that certain antihypertensive medications, including those that target the renin-angiotensin system (RAS), are associated with a decreased risk of dementia. This study assesses whether two FDA-approved RAS-targeting drugs: telmisartan [a moderately brain-penetrant angiotensin receptor blocker (ARB)], and lisinopril [a brain-penetrant angiotensin-converting enzyme (ACE) inhibitor]; can be repurposed for the treatment of CAA.

METHODS: At either ∼3 months (early intervention) or ∼8 months (later intervention) of age, male and female Tg-SwDI mice began treatment with either telmisartan (1 mg/kg/day) or lisinopril (15 mg/kg/day) dissolved in drinking water or received plain drinking water only. Age- and sex-matched C57BL/6J mice receiving plain drinking water served as wild-type controls. Following 4 months of treatment, mice underwent blood pressure measurement followed by behavioral testing prior to euthanasia.

RESULTS: Voluntary oral consumption delivered doses similar to the target dose for both drugs. At the doses used, telmisartan and lisinopril treatment did not significantly reduce blood pressure in Tg-SwDI mice. Our findings thus far suggest that these drug treatments, particularly lisinopril, may mitigate cognitive-behavioral deficits observed in Tg-SwDI mice.

CONCLUSIONS: Ongoing experiments are being completed to increase sample sizes and investigate the potential benefits of telmisartan and lisinopril to mitigate neuropathological and cognitive impairment in Tg-SwDI mice. If findings support our hypothesis, this will demonstrate that these drugs could be repurposed to prevent and/or treat CAA, reducing the worldwide burden of stroke and dementia.

PMID:39783163 | DOI:10.1002/alz.095709

Alzheimers Dement. 2024 Dec;20 Suppl 6:e089679. doi: 10.1002/alz.089679.

ABSTRACT

BACKGROUND: Developing drugs for treating Alzheimer's disease (AD) has been extremely challenging and costly due to limited knowledge on underlying biological mechanisms and therapeutic targets. Repurposing drugs or their combination has shown potential in accelerating drug development due to the reduced drug toxicity while targeting multiple pathologies.

METHOD: To address the challenge in AD drug development, we developed a multi-task machine learning pipeline to integrate a comprehensive knowledge graph on biological/pharmacological interactions and multi-level evidence on drug efficacy, to identify repurposable drugs and their combination candidates RESULT: Using the drug embedding from the heterogeneous graph representation model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, mechanistic efficacy in preclinical models, population-based treatment effect, and Phase 2/3 clinical trials. We experimentally validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations.

CONCLUSION: This methodology showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

PMID:39782643 | DOI:10.1002/alz.089679

Alzheimers Dement. 2024 Dec;20 Suppl 6:e086778. doi: 10.1002/alz.086778.

ABSTRACT

BACKGROUND: Despite increasing knowledge of the etiology of neurodegenerative diseases, translation of these benefits into therapeutic advances for Alzheimer's Disease and related diseases (ADRD) has been slow. Drug repurposing is a promising strategy for identifying new uses for approved drugs beyond their initial indications. We developed a high-throughput drug screening platform aimed at identifying drugs capable of reducing proteotoxicity in vivo (Aß toxicity in Caenorhabditis elegans) AND inhibiting microglial inflammation (TNF-alpha IL-6), both implicated in driving AD(figure attached with sample of results in C. elegans). These screens led us to prioritize 50 potentially protective FDA-approved drugs. We propose to test our screening results in humans using administrative claims data collected from the Centers for Medicare and Medicaid Services METHOD: This is an observational retrospective pharmaco-epidemiological longitudinal cohort study. The cohort is a random sample of 1,000,000 beneficiaries, aged 65-75 years, followed for 10 consecutive years, requested from CMS. Files include MedPar, Outpatient, Carrier, Hospice to maximize inclusion of AD beneficiaries according to Bynum algorithm, and Part D event for drug prescription details. We will use Cox regression, to compute Hazard Ratios and associated 95% confidence intervals, of the association between drug exposure status and the risk of ADRD. We will examine potential confounding by indication, drug target, and competing risks.

RESULT: 1/We propose to assess if drugs which reduce Ab toxicity in a C. elegans model of AD AND reduce microglial inflammation reduce the risk of developing ADRD in humans, using Medicare claims. 2/We propose to assess if drugs which reduce inflammation, reduce the risk of developing ADRD in humans, using the Medicare claims.

CONCLUSION: The end goal of the study is to identify drugs to be repurposed to treat ADRD and to accumulate strong epidemiological evidence in addition to the existing evidence from the model organism C. elegans and cell culture studies.

PMID:39782522 | DOI:10.1002/alz.086778

Alzheimers Dement. 2024 Dec;20 Suppl 6:e087380. doi: 10.1002/alz.087380.

ABSTRACT

BACKGROUND: Global epidemiological studies involving over nine million participants have shown a 35% lower incidence of Alzheimer's Disease (AD) in older cancer survivors compared to those without a history of cancer. This inverse relationship, consistent across recent studies with methodological controls, suggests that cancer itself, rather than cancer treatments, may offer protective factors against AD. This insight opens avenues for novel therapeutic strategies targeting early AD by harnessing cancer-associated protective factors.

METHODS: To investigate the potential protective effect of cancer against Alzheimer's Disease (AD), we developed "cancer-in-AD" mouse models. These models involved injecting a small number of breast cancer cells into young AD-model mice (5xFAD) and monitoring amyloid plaque progression. Additionally, we introduced extracellular vesicles (EVs) from breast tumor-bearing mice into similar AD models. Using spatial transcriptomics, we analyzed brain tissue gene expression and cell-cell interactions, focusing on the astrocyte-microglia-oligodendrocyte network near amyloid plaques. This approach helped identify potential drugs for repurposing in AD treatment.

RESULTS: The study found a significant reduction in amyloid burden within the brains of the cancer-in-AD mouse models compared to age-matched cancer-free AD mice. The administration of EVs from cancer animal's plasma to the AD mice prompted the release of various inflammatory cytokines and chemokines. A key discovery was an activated astrocyte-microglia-oligodendrocyte signaling network that regulates amyloid-beta homeostasis in these mouse brains. Out of 49 FDA-approved drugs identified to induce this cancer-induced signaling, 11 showed promise in improving AD symptoms and reducing amyloid and tau accumulations, in both preclinical and clinical studies.

CONCLUSIONS: The study reveals a notable decrease in amyloid levels in AD mice with cancer or exposed to tumor-derived EVs, linked to immune system reprogramming and glial network activation. This supports the study's drug repositioning approach and sets the stage for further research into the anti-AD properties of these drugs, focusing on identifying crucial signaling elements for enhanced drug repositioning and combination treatment strategies.

PMID:39782516 | DOI:10.1002/alz.087380

Alzheimers Dement. 2024 Dec;20 Suppl 6:e089290. doi: 10.1002/alz.089290.

ABSTRACT

BACKGROUND: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.

METHOD: The graph neural network framework integrates four layers of input data including transcriptome, proteome, drug, and subject to connect to bipartite graphs in the United Kingdom Biobank (UKBB). Medication usage, clinical, and GWAS data were downloaded for 48187 subjects with first and second cognitive exams from the UKBB. The protein and transcriptome layers were constructed using the String database and gene coexpression networks generated from single nuclei RNA data (Sahelijo et al. 2022). Layers were connected by binary bipartite graphs constructed using drug information from the UniProt and DrugCentral databases. Sequential Graph Attention Networks convoluted embedded features generated by each layer in a hierarchical order: 1. gene-gene, 2. gene-protein, 3. protein-protein, 4. protein-drug, 5. drug-subj. Feature embeddings were decoded using a multilayer perceptron to predict cognitive improvement between the first and second cognitive exams. Two models compared methods of data aggregation. The first model follows a strict hierarchy, aggregating inter-layer data in a single direction (gene > protein > drug > subj). The second model allows for fluid message passing between inter-layers. We used 60% of the UKBB subjects for training and 30% for validation. We assessed the model with the greatest training accuracy using the remaining 10% of subjects.

RESULT: We observed that the performance of the strict message-passing model attained validation and test accuracy of 57.7% and 51.2%, respectively. Performance of the fluid message passing model improved prediction accuracy with 61.3% and 58.1% in the test set.

CONCLUSION: Our investigation suggests the feasibility of the PreSiBOGNN framework to infer cognitive improvement of existing drugs by integrating medication, multi-omics, and clinical data. Future work will focus on model optimizations and the integration of additional modalities including compound-specific fingerprint data.

PMID:39782415 | DOI:10.1002/alz.089290

Alzheimers Dement. 2024 Dec;20 Suppl 6:e090350. doi: 10.1002/alz.090350.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) presents challenges with its complex neurodegenerative mechanisms, leading to a high failure rate in clinical trials. While drug repositioning offers a cost-effective solution, the lack of a subtype-driven strategy hinders success. Previously, we defined genetic subtypes and their prioritized genes for each genetic subtype (Sahelijo et al., 2022). This study evaluated unsupervised learning algorithms to characterize existing compounds targeting prioritized genes for the genetic subtypes.

METHOD: Compounds in at least Phase 2 clinical trials were gathered from PubChem. Active compounds against subtype-specific genes were selected, and their structural data were transformed into pharmacophore fingerprints using ChemmineR. Unsupervised algorithms (Agglomerative Clustering, Ensemble Clustering, Gaussian Mixture Models, Bayesian Gaussian Mixture Models) were optimized using evaluation metrics (Calinski-Harabasz, Davies-Bouldin, Silhouette) to cluster compounds within each subtype with the optimal cluster number and optimal algorithm. The finalized clusters of compounds were evaluated using significance values and mean within-cluster Jaccard similarity scores and were characterized with target genes and mechanisms of action.

RESULT: Four of the nine genetic subtypes generated compound clusters, including 3 clusters using Agglomerative Clustering for Ast-M2 with 11 targets and 180 compounds, 2 clusters using BGMM for Ast-M9 with 14 targets and 341 compounds, and 4 clusters using Ensemble Clustering for Oli-M45 with 11 targets and 66 compounds and for Oli-M50 with 18 genes and 431 compounds. We observed common structural signatures between Ast-M2, Ast-M9, and Oli-M50 clusters, while Oli-M45 clusters did not share any signatures with other clusters. The most significant cluster was found for the Oli-M45 subtype, with a cluster significance value of 8.49 and the highest mean compound similarity score of 0.96. The top-ranked cluster primarily contained Vinblastine formulations-microtubule and tubulin polymerization inhibitors-targeting TUBA1A and TUBA1B.

CONCLUSION: We demonstrated a novel drug repositioning framework for AD using unsupervised learning algorithms, enabling precision medicine and subtype-driven repositioning. This framework will be implemented in our future software tools.

PMID:39782372 | DOI:10.1002/alz.090350

J Inherit Metab Dis. 2025 Jan;48(1):e12835. doi: 10.1002/jimd.12835.

ABSTRACT

Inborn errors of metabolism (IEMs) are rare genetic conditions with significant morbidity and mortality. Technological advances have increased therapeutic options, making it challenging to remain up to date. A centralized therapy knowledgebase is needed for early diagnosis and targeted treatment. This study aimed to identify all treatable IEMs through a scoping literature review, followed by data extraction and analysis according to the Treatabolome principles. Knowledge of treatable IEMs, therapeutic categories, efficacy, and evidence was integrated into the Inborn Errors of Metabolism Knowledgebase (IEMbase), an online database encompassing all IEMs. The study identified 275 treatable IEMs, 18% of all currently known 1564 IEMs, according to the International Classification of Inherited Metabolic Disorders. Disorders of fatty acid and ketone body metabolism had the highest treatability (67%), followed by disorders of vitamin and cofactor metabolism (60%), and disorders of lipoprotein metabolism (42%). The most common treatment strategies were pharmacological therapy (34%), nutritional therapy (34%), and vitamin and trace element supplementation (12%). Treatment effects were most commonly observed in nervous system abnormalities (34%), metabolism/homeostasis abnormalities (33%), and growth (7%). Predominant evidence sources included case reports with evidence levels 4 (48%) and 5 (12%), and individual cohort studies with evidence level 2b (12%). Our study generated the Metabolic Treatabolome 2024. IEMs are the largest group of monogenic disorders amenable to disease-modifying therapy. With drug repurposing efforts and advancements in gene therapies, this number will expand. IEMbase now provides up-to-date, comprehensive information on clinical and biochemical symptoms and therapeutic options, empowering patients, families, healthcare professionals, and researchers in improving patient outcomes.

PMID:39777714 | DOI:10.1002/jimd.12835

Theranostics. 2025 Jan 1;15(3):1122-1134. doi: 10.7150/thno.101358. eCollection 2025.

ABSTRACT

Rationale: Acute liver failure (ALF) is characterized by rapid hepatic dysfunction, primarily caused by drug-induced hepatotoxicity. Due to the lack of satisfactory treatment options, ALF remains a fatal clinical disease, representing a grand challenge in global health. Methods: For the drug repositioning to ALF of mesalamine, which is clinically approved for the treatment of inflammatory bowel disease (IBD), we propose a supramolecular prodrug nanoassembly (SPNs). Mesalamine is modified with a functional peptide of the FRRG sequence. The resulting mesalamine prodrugs form nanoassemblies solely through intermolecular interactions, ensuring high drug loading capacity and reducing the potential toxicity associated with the carrier materials of conventional nanoparticle systems. Results: In acetaminophen (APAP)-induced ALF mouse models, the SPNs predominantly accumulate in injured target tissues owing to the nanoparticles' propensity to target the liver. Subsequently, cathepsin B overexpressed in hepatocytes by drug-induced inflammation triggers the release of mesalamine from the nanoassemblies via enzymatic cleavage, resulting in remarkable therapeutic efficacy. Meanwhile, nonspecific drug release in healthy cells is inhibited due to their relatively lower cathepsin B expression, which helps prevent the exacerbation of the ALF by minimizing adverse events related to drug exposure. Conclusions: This study provides valuable insights into designing rational nanomedicine for repurposing mesalamine in ALF treatment, potentially inspiring further research to discover effective and safe therapeutic options for patients.

PMID:39776792 | PMC:PMC11700871 | DOI:10.7150/thno.101358

Fundam Clin Pharmacol. 2025 Feb;39(1):e13046. doi: 10.1111/fcp.13046.

NO ABSTRACT

PMID:39775992 | DOI:10.1111/fcp.13046

Pharm Res. 2025 Jan 7. doi: 10.1007/s11095-024-03811-1. Online ahead of print.

ABSTRACT

Metabolic alterations are commonly associated with various cancers and are recognized as contributing factors to cancer progression, invasion, and metastasis. Drug repurposing, a strategy in drug discovery, utilizes existing knowledge to recommend established drugs for new indications based on clinical data or biological evidence. This approach is considered a less risky alternative to traditional drug development. Metformin, a biguanide, is a product of Galega officinalis (French lilac) primarily prescribed for managing type 2 diabetes, is recognized for its ability to reduce hepatic glucose production and enhance insulin sensitivity, particularly in peripheral tissues such as muscle. It also improves glucose uptake and utilization while decreasing intestinal glucose absorption. Statins, first isolated from the fungus Penicillium citrinum is another class of medication mainly used to lower cholesterol levels in individuals at risk for cardiovascular diseases, work by inhibiting the enzyme 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, which is essential for cholesterol biosynthesis in the liver. Metformin is frequently used in conjunction with statins to investigate their potential synergistic effects. Combination of metformin and simvastatin has gathered much attention in cancer research because of its potential advantages for cancer prevention and treatment. In this review, we analyze the effects of metformin and simvastatin, both individually and in combination, on key cancer hallmarks, and how this combination affects the expression of biomolecules and associated signaling pathways. We also summarize preclinical research, including clinical trials, on the efficacy, safety, and potential applications of repurposing metformin and simvastatin for cancer therapy.

PMID:39775614 | DOI:10.1007/s11095-024-03811-1

Elife. 2025 Jan 8;12:RP92491. doi: 10.7554/eLife.92491.

ABSTRACT

There are thousands of Mendelian diseases with more being discovered weekly and the majority have no approved treatments. To address this need, we require scalable approaches that are relatively inexpensive compared to traditional drug development. In the absence of a validated drug target, phenotypic screening in model organisms provides a route for identifying candidate treatments. Success requires a screenable phenotype. However, the right phenotype and assay may not be obvious for pleiotropic neuromuscular disorders. Here, we show that high-throughput imaging and quantitative phenotyping can be conducted systematically on a panel of C. elegans disease model strains. We used CRISPR genome-editing to create 25 worm models of human Mendelian diseases and phenotyped them using a single standardised assay. All but two strains were significantly different from wild-type controls in at least one feature. The observed phenotypes were diverse, but mutations of genes predicted to have related functions led to similar behavioural differences in worms. As a proof-of-concept, we performed a drug repurposing screen of an FDA-approved compound library, and identified two compounds that rescued the behavioural phenotype of a model of UNC80 deficiency. Our results show that a single assay to measure multiple phenotypes can be applied systematically to diverse Mendelian disease models. The relatively short time and low cost associated with creating and phenotyping multiple strains suggest that high-throughput worm tracking could provide a scalable approach to drug repurposing commensurate with the number of Mendelian diseases.

PMID:39773880 | DOI:10.7554/eLife.92491

BMC Bioinformatics. 2025 Jan 7;26(1):5. doi: 10.1186/s12859-024-06032-w.

ABSTRACT

The process of new drug development is complex, whereas drug-disease association (DDA) prediction aims to identify new therapeutic uses for existing medications. However, existing graph contrastive learning approaches typically rely on single-view contrastive learning, which struggle to fully capture drug-disease relationships. Subsequently, we introduce a novel multi-view contrastive learning framework, named CDPMF-DDA, which enhances the model's ability to capture drug-disease associations by incorporating diverse information representations from different views. First, we decompose the original drug-disease association matrix into drug and disease feature matrices, which are then used to reconstruct the drug-disease association network, as well as the drug-drug and disease-disease similarity networks. This process effectively reduces noise in the data, establishing a reliable foundation for the networks produced. Next, we generate multiple contrastive views from both the original and generated networks. These views effectively capture hidden feature associations, significantly enhancing the model's ability to represent complex relationships. Extensive cross-validation experiments on three standard datasets show that CDPMF-DDA achieves an average AUC of 0.9475 and an AUPR of 0.5009, outperforming existing models. Additionally, case studies on Alzheimer's disease and epilepsy further validate the model's effectiveness, demonstrating its high accuracy and robustness in drug-disease association prediction. Based on a multi-view contrastive learning framework, CDPMF-DDA is capable of integrating multi-source information and effectively capturing complex drug-disease associations, making it a powerful tool for drug repositioning and the discovery of new therapeutic strategies.

PMID:39773275 | DOI:10.1186/s12859-024-06032-w

J Transl Med. 2025 Jan 7;23(1):25. doi: 10.1186/s12967-024-06046-1.

ABSTRACT

BACKGROUND: Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement.

METHODS: This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database.

RESULTS: We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFC) that the drug candidates could induce. Among five prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells.

CONCLUSIONS: The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.

PMID:39773231 | DOI:10.1186/s12967-024-06046-1