Drug Database
DI

dihydroergocristine (Diertina / Decme)

✓ Approved

Poli · therapeutic agent

What is dihydroergocristine?

dihydroergocristine is a therapeutic agent developed by Poli. It is approved for therapeutic indications via oral (po).

Drug Profile

Brand NamesDiertina, Decme
CompanyPoli
RouteOral (PO)
StatusApproved
Sources: ClinicalTrials.gov, Poli

Therapeutic Indications

dihydroergocristine is developed for 1 unique indication across 1 therapeutic area.

Therapeutic AreaConditionPhase
Nervous system disordersVascular dementia✓ Approved

Related Research Articles

PubMedNaunyn-Schmiedeberg's archives of pharmacology2026-05-03

In silico analysis of gene expression signatures and drug repurposing associated with metastatic progression in melanoma (skin cancer).

Majeed Khulood Ayad KA, Majeed Raghad Ayad RA, Ibrahim Taisir Khalil TK, Khan Najeeb Ullah NU

Metastatic melanoma is an aggressive, heterogeneous cancer with early spread and poor prognosis. Transcriptomic analysis identifies potential therapeutic targets. In silico analysis of the GEO dataset GSE7553 compared primary vs metastatic melanoma using differential expression, enrichment (GO/KEGG/Reactome), PPI network construction, and hub-gene prioritization. Candidates were validated through survival analysis, mutation-associated analyses, and virtual screening using molecular docking with FDA-approved compounds. Transcriptomic results show divergence between primary and metastatic melanoma samples, with principal component analysis supporting clear group separation. In a total of 54,675 probe-level entries, 4868 were classified as upregulated and 10,269 as downregulated, indicating a predominance of downregulated transcriptional events in metastatic melanoma. Prioritized upregulated genes included CUL5, ZC3H14, SON, BRCC3, and H3-3B, whereas notable downregulated genes included ZNF709, CD84, STARD8, EPOR, and HAVCR2. The high-confidence PPI network comprised 625 nodes and 2661 edges, with a significant enrichment score. Enrichment analysis implicated immune/adhesion and translation pathways (e.g., Rap1, focal adhesion, T-cell activation). Survival: CUL5 (HR = 0.26) and ZC3H14 (HR = 0.60) are protective, while SON (HR = 2.4) is adverse. Mutation-linked transcriptomic analysis identified 10 significantly altered genes, including downregulated SNHG18 and upregulated LPCAT2. Virtual screening results show repurposable compounds, with Floxacrine showing strong predicted affinity for CUL5 and Dihydroergocristine showing favorable interaction with LPCAT2/ZC3H14-related targets. In silico docking results further supported CUL5-Floxacrine and LPCAT2-Dihydroergocristine as notable candidate interactions. Results show key transcriptomic drivers and targets (CUL5, ZC3H14, SON, BRCC3, LPCAT2) in metastatic melanoma. Results highlight a useful hypothesis-generating framework for biomarker prioritization and drug repurposing in melanoma. However, independent cohort validation and experimental confirmation are required before clinical translation.

PMID 42068380
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PubMedJournal of pharmaceutical analysis2025-07-18

Perturbation response scanning of drug-target networks: Drug repurposing for multiple sclerosis.

Lu Yitan Y, Zhou Ziyun Z, Li Qi Q, Yang Bin B et al.

Combined with elastic network model (ENM), the perturbation response scanning (PRS) has emerged as a robust technique for pinpointing allosteric interactions within proteins. Here, we proposed the PRS analysis of drug-target networks (DTNs), which could provide a promising avenue in network medicine. We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework, for drug repurposing of multiple sclerosis (MS). First, the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes. Then, based on topological analysis and functional annotation, the neurotransmission module was identified as the "therapeutic module" of MS. Further, perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis, giving a list of repurposable drugs for MS. Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of serotonin 2B receptor (HTR2B). Finally, we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex. These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS. As a useful systematic method, our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.

PMID 40678478
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PubMedTranslational cancer research2025-06-18

Identification of neuronal synapse-related signatures and potential therapeutic drugs in colorectal cancer based on machine learning algorithms and molecular docking.

Wu Wen-Jing WJ, Wang Kan K, Yang Yang Vivian YV, Yang Xiaoning X

Nervous system-cancer interactions can regulate tumorigenesis, invasion, and metastasis. However, specific biomarkers for targeting neuron synapse in colorectal cancer (CRC) remain unexplored. This study aims to develop a neuronal synapse-related signature (NSRS) to predict survival in CRC patients, identify potential therapeutic drugs, and explore its clinical applications. We collected neuronal synapse genes (NSGs) from the Molecular Signatures Database (MSigDB) and published mass spectrometry data. Using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator Cox regression (LASSO-Cox), we identified prognostic NSGs and constructed a NSRS through multivariate Cox regression. Functional enrichment analysis revealed the molecular characteristics of NSRS subgroups. Additionally, xCell and ESTIMATE algorithms quantified the abundance of 54 cell subtypes and assessed the tumor immune microenvironment (TIME) of the two NSRS subgroups. Finally, drug prediction and molecular docking identified candidate drugs with therapeutic potential. Seven key prognostic NSGs were identified, and an independent, stable NSRS model was constructed. Kaplan-Meier survival curves indicated that the high NSRS group had poorer outcomes (log-rank test, P<0.05). Functional enrichment analysis revealed significant enrichment of epithelial-mesenchymal transition, hypoxia, and inflammation features in the high NSRS group. xCell and ESTIMATE analyses showed a more complex TIME and lower tumor purity in the high NSRS group, highlighting the role of neuro-tumor interactions in CRC. Drug prediction and molecular docking suggested alprostadil, dihydroergocristine, and nocodazole as candidate drugs for CRC treatment. This is the first study to develop neuron synapse-related biomarkers from the perspective of neuron-cancer interactions using machine learning. We constructed a robust NSRS model and identified candidate drugs targeting prognostic NSGs, providing new insights into CRC prognosis and treatment.

PMID 40530126
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PubMedCurrent medicinal chemistry2025-06-04

A Genetic Perspective to Reveal the Impact of Mitochondrial Dysfunction-related Genes on Diabetic Kidney Disease: A Multi-omics Study.

Zhang Yan Y, Wang Zeyuan Z, Shang Jin J, Dong Yijun Y et al.

This study investigated the causes of Mitochondrial Dysfunction (MD) in Diabetic Kidney Disease (DKD) progression, and identified genes associated with DKD, especially those with significant genetic causal effects, to provide a theoretical basis for DKD treatment. Using a large database and single-cell RNA sequencing (scRNA-seq) data, 333 MDRDEGs were discovered. MDRDEGs were linked to AGE-RAGE signaling, RNA processing, protein transport, and energy metabolism using functional enrichment analysis. Seven MDRDEGs with significant genetic causal effects in DKD were discovered using SMR and MR analyses: ACTN1, ALG11, CCNB1, HIVEP2, MANBA, TUBA1A, and WFS1. Co-localization and scRNA-seq analyses examined these genes' DKD connections. Due to the high significance of its prediction model and DKD expression, ACTN1 was studied in depth. PheWAS and molecular dynamics analysis assessed ACTN1's safety and efficacy as a therapeutic target, and its connection with other symptoms. ACTN1 protein expression in DKD tissues was confirmed by immunofluorescence. Functional enrichment analysis revealed that MDRDEGs were mostly related to AGE-RAGE signaling, RNA processing, protein transport, and energy metabolism. Seven MDRDEGs caused DKD genetically in SMR and MR investigations. Genetic variations in ACTN1, ALG11, MANBA, and TUBA1A were linked to DKD by co-localization studies. scRNA-seq showed a dramatic increase in ACTN1 expression in DKD. Molecular dynamics analysis demonstrated that Dihydroergocristine can safely bind to ACTN1, while the PheWAS investigation found no significant relationships. DKD tissues exhibited higher ACTN1 protein levels via immunofluorescence. This study identified MDRDEGs linked to inflammation, cytoskeletal stabilization, and glucose metabolism pathways critical in Diabetic Kidney Disease (DKD) pathogenesis, highlighting their clinical potential as therapeutic targets. Notably, ACTN1 emerged as a causally linked gene overexpressed in DKD, with the prediction of dihydroergocristine as a targeting compound, offering novel avenues for clinical intervention. This study suggests that ACTN1 may be a therapeutic target for DKD and sheds light on its molecular pathogenesis, clinical prevention, and treatment.

PMID 40464186
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PubMedHeliyon2025-01-21

Untangling the complex mechanisms associated with Alzheimer's disease in elderly patients using high-throughput RNA sequencing data and next-generation knowledge discovery methods: Focus on potential gene signatures and drugs for dementia.

Alkhatabi Hind A HA, Pushparaj Peter Natesan PN

Alzheimer's disease (AD) is a complex neurodegenerative disorder that primarily affects elderly individuals. This study aimed to elucidate the intricate mechanisms underlying AD in elderly patients compared with healthy aged individuals using high-throughput RNA sequencing (RNA-seq) data and next-generation knowledge discovery methods (NGKD), with a focus on identifying potential therapeutic agents. High-throughput RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE104704). These data were derived from healthy and diseased human brains (eight young healthy brains [young], 10 aged healthy brains [Old], and 12 aged diseased brains [AD]). We used NGKD tools such as GEO RNA-seq Experiments Interactive Navigator (GREIN) to obtain differentially expressed genes (DEGs) by comparing the AD versus Old RNA-seq data and further filtered and normalized to obtain differentially regulated Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome and Panther pathways using ExpressAnalyst tool. Besides, WebGestalt was used to identify differentially regulated Gene Ontologies (GO) and the pre-ranked Gene Set Enrichment Analysis (GSEA) was performed using GSEA software. The X2K web tool was used to infer upstream regulator networks and X2K Appyter tool for obtaining transcription factors (TFs) and kinase network information. LFW1000 and L1000CDS2 tools were used to identify specific drugs that reverse AD-associated gene signatures in elderly patients. Our study revealed significant downregulation of pathways related to neuroactive receptor-ligand interaction, synaptic vesicle cycle, and neuronal system in elderly individuals with AD. GO analysis showed negative enrichment of functions related to cognition, potassium ion transport, receptor-ligand activity, SNARE binding, and primary lysosomes. The transcription factors SUZ12 and REST, along with increased MAPK signaling, were identified as key regulators of downregulated genes. Several drugs and natural products, including dihydroergocristine, mepacrine, gedunin, amlodipine, and disulfiram have been identified as potential therapeutic agents for reversing AD-associated gene signatures. This comprehensive analysis of AD in elderly individuals using RNA-seq data and NGKD tools revealed multiple differentially regulated pathways, gene signatures, and potential drugs for dementia treatment. These findings highlight the complex molecular mechanisms underlying AD and provide insights into potential therapeutic strategies. Further research is needed to validate these findings and to develop personalized treatment approaches for AD in elderly patients.

PMID 39834440
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PubMedNatural product research2024-05-17

Molecular docking based comparative study of antiviral compounds on SARS-CoV-2 spike protein.

Nagavarapu Sowmya S, Kumar Jitendra J, Biswal Pradyut K PK

The urgent need for effective therapeutic interventions against SARS-CoV-2 has prompted extensive exploration of potential drug candidates. Among the viral proteins, the spike (S) protein presents an attractive target due to its critical role in viral entry and infection. In this study, we employed molecular docking techniques to investigate the binding affinities and interaction profiles of a panel of active compounds against the SARS-CoV-2 spike protein. Utilising computational simulations, we assessed the binding properties of these compounds within the receptor-binding domain (RBD) and other key regions of the spike protein. Our comparative analysis elucidates the differential binding patterns and identifies promising lead compounds with high binding affinity and favourable interaction profiles. Furthermore, we discuss the implications of these findings for the development of potential therapeutics targeting the SARS-CoV-2 spike protein. Using molecular docking and the Lipinski five rule, this study illustrates possible compounds with strong binding affinities, their molecular interactions, for both naturally occurring and man-made drugs. Computational approach is applied, and it is concluded that, drugs like Withanolide, Dihydroergocristine, Fenebrutinib, and Ergotamine shows binding energies between -8.3 and -9.1 kcal/mol, and are possible candidate for anti covid drug.

PMID 38759218
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