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

Therapeutic Indications

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

Therapeutic AreaConditionPhase
Nervous system disordersVascular dementia✓ Approved

Related Research Articles

PubMedMicrobiology spectrum2026-10-09

Exploiting Neosartorya (Aspergillus) fischeri antifungal protein 2-defined fungal targets for drug repurposing against Candida albicans.

Dán Kinga K, Papp Rebeka R, Tóth Liliána L, Miszlai Lizett L et al.

Invasive fungal infections caused by Candida albicans remain major clinical challenges because of the limited therapeutic options, toxicity, and emerging resistance. Drug repurposing offers a promising strategy to accelerate antifungal discovery by leveraging existing compounds with known safety profiles. In this study, we applied a structure-based drug repurposing approach guided by the antifungal protein Neosartorya (currently Aspergillus) fischeri antifungal protein 2 (NFAP2), which targets fungus-specific intracellular proteins, including glutamate decarboxylase, mitochondrial ATP synthase subunit alpha, and enolase. NFAP2-associated binding pockets were identified and used to virtually screen approved clinically investigated compounds. Selected candidates were further evaluated by molecular docking, followed by in vitro antifungal susceptibility testing and interaction analysis with conventional antifungal agents. In vivo toxicity and therapeutic efficacy were assessed using a Galleria mellonella infection model. Despite predicted binding affinities, most repurposed compounds exhibited limited antifungal activity in vitro, highlighting a disconnect between in silico predictions and biological efficacy. Among the tested compounds, dihydroergocristine mesylate (DHEC) and meclizine dihydrochloride exhibited measurable antifungal effects. Notably, DHEC demonstrated pronounced synergistic interactions with amphotericin B, resulting in near-complete growth inhibition in vitro. In vivo, DHEC + amphotericin B induced no detectable toxicity and significantly improved host survival compared with either drug alone. These findings indicate that NFAP2-guided structure-based drug repurposing can identify compounds targeting fungus-specific vulnerabilities. However, antifungal activity depends on factors beyond binding affinity. Our results highlight integrating computational screening with experimental validation and support repurposed compounds as adjuvants in combination therapies to enhance antifungal efficacy and translational potential.Invasive fungal infections caused by Candida albicans remain a major clinical challenge due to limited therapeutic options, toxicity, and emerging resistance. Drug repurposing offers a promising strategy to accelerate antifungal discovery by leveraging compounds with known safety profiles. A structure-based repurposing framework guided by the antifungal protein NFAP2, which targets fungus-specific intracellular proteins, is presented. Using these biologically informed targets, clinically relevant compounds with antifungal and adjuvant potential were identified. Predicted binding affinity alone did not reliably translate into antifungal activity, highlighting the need for integrated computational and experimental approaches. Notably, dihydroergocristine mesylate (DHEC) showed strong synergistic effects with amphotericin B, resulting in near-complete growth inhibition and improved host survival in vivo. These findings highlight the potential of repurposed compounds as adjuvants and support combination-based strategies targeting fungal-specific vulnerabilities. Furthermore, this work provides a framework for the future development of DHEC-inspired antifungal adjuvant strategies.

PubMedInternational journal of biological macromolecules2026-10-05

Targeting the allosteric mechanism of methicillin-resistant Staphylococcus aureus pyruvate kinase: From inhibitor discovery to network reorganization.

Yuce Merve M, Turkmenoglu Ipek I, Kurtulus Gamze G, Sesal Cenk C et al.

Pyruvate kinase (PK) is a promising antimicrobial target against Methicillin-resistant Staphylococcus aureus (MRSA) due to its central role in bacterial metabolism. Building on previous virtual screening of three putative/known allosteric sites, we evaluated six FDA-approved compounds for effects on PK-associated enzymatic activity in a bacterial extract system. Four compounds (Dihydroergocristine, Nilotinib, Zanubrutinib, and Ledipasvir) reduced measured activity, whereas Dihydroergotamine and Simeprevir produced modest increases in assay signal. Complementary computational analyses investigated the potential structural and dynamic basis of these activity changes. Elastic network-based AlloSigMA2 indicated similar allosteric responses at the subunit interfaces, while the putative allosteric site was preferentially linked to active-site stabilization. Molecular dynamics (MD) simulations, mutual information and network analyses focused on Zanubrutinib and Simeprevir, with inhibitor 09C and the apo-state as controls. Zanubrutinib remained relatively stable at the inter-subunit interfaces and was associated with inactive-like dynamic features, including strengthened inter-domain coupling and inter-chain correlations. Conversely, Simeprevir was associated with site-dependent active-like dynamic features: binding at the putative allosteric site redistributed correlations toward intra-chain couplings, whereas binding at the large interface markedly reduced long-range correlations, consistent with localized or decoupled communication modes. The apo-state exhibited a pre-organized, effector-centered communication network. Collectively, these findings support a model in which the functional effects of putative allosteric ligands on S. aureus PK may be associated with site-dependent reorganization of its residue communication network. The identified compounds and their predicted communication footprints provide a foundation for further experimental validation and development of species-selective inhibitors as potential next-generation antimicrobial agents.

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.

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.

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.

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.

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