Evidence map›Paper›PMID 40114577›Full record

ArticleCurrent pharmaceutical design2025

Computational Screening of IL-1 and IL-6 Inhibitors for Rheumatoid Arthritis: Insights from Molecular Docking and Dynamics Analysis.

Yunwei Li, Salam Pradeep Singh

Abstract read
PubMed Publisher
In one paragraph

Article in Current pharmaceutical design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yunwei LiInstitute of Science and Technology, Beijing Open University, A4 Zaojunmiao, Haidian District, Beijing, 100081, China.ORCID 0009-0000-6440-3202
Salam Pradeep SinghDepartment of Bioinformatics, Suchee Bioinformatics Institute, Imphal West, Manipur, 795001, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRheumatoid arthritis (RA) remains a significant therapeutic challenge due to its chronic inflammatory nature. Consequently, many patients turn to alternative therapies, such as herbal compounds and supplements, when conventional treatments prove relatively ineffective or cause adverse side effects. Some compounds are being investigated for their potential to alleviate RA symptoms or manage disease. This study aimed to evaluate the anti-inflammatory effects of selected herbal compounds targeting the Interleukin-1 (IL-1) and Interleukin-6 (IL-6) pathways, key inflammatory regulators in RA. Specifically, the study assessed the binding affinity, stability, and dynamics of IL-1 and IL-6 inhibitory compounds as potential therapeutic agents for RA.

methodsIn silico experiments were conducted with herbal compounds to modulate IL-1 and IL-6 signaling. Computational techniques, including molecular docking, molecular dynamics (MD) simulations, Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) calculations, Absorption, Distribution, Metabolism, and Excretion (ADME) analysis, toxicity predictions, and Density Functional Theory (DFT) analysis, were employed to investigate these interactions comprehensively.

resultsNeoglucobrassicin demonstrated the strongest binding affinity for IL-6 (Total score: -349.00 kJ/mol), followed by Galbelgin (-338.00 kJ/mol). For IL-1β, CID21722980 exhibited the highest binding affinity (-273.14 kJ/mol), with Eupaformosanin ranking second (-264.29 kJ/mol). Neoglucobrassicin formed interactions with multiple IL-6 residues, indicating a stable binding complex, while CID21722980 similarly interacted with key IL-1β residues, forming stable complexes. Both the Neoglucobrassicin-IL-6 and CID21722980- IL1β complexes demonstrated structural stability, as evidenced by Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) stabilizing towards the end of the 100 ns molecular dynamics (MD) simulation. MM-GBSA analysis revealed the highest binding energy for the IL-6-Neoglucobrassicin complex (-43.70 kcal/mol), while CID21722980 showed strong affinity for IL-1β (-43.29 kcal/mol), suggesting enhanced binding potential. Additionally, Density Functional Theory (DFT) analysis of the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) energies revealed electron distribution patterns in Neoglucobrassicin and CID21722980 that support their potential therapeutic applications. DISCUSSION: The strong binding affinities, stable molecular dynamics (MD) simulations, and favorable ADMET and DFT properties of Neoglucobrassicin and CID21722980 underscore their potential as antiinflammatory agents targeting IL-6 and IL-1β. The mechanistic insights into their inhibitory effects on these targets suggest multifaceted anti-inflammatory properties, warranting further in vivo and clinical investigations.

conclusionNeoglucobrassicin and CID21722980 demonstrated promising binding affinities, favorable pharmacokinetic profiles, and advantageous electronic properties, positioning them as strong candidates for further exploration in anti-inflammatory therapies. These findings highlight the potential of these herbal compounds as modulators of IL-6 and IL-1β, paving the way for future drug development.

Indexed as

Antirheumatic AgentsArthritis, RheumatoidInterleukin-1Interleukin-6Interleukin-6 InhibitorsMolecular Docking SimulationMolecular Dynamics SimulationDrug Evaluation, PreclinicalHumansAntirheumatic AgentsIL6 protein, humanInterleukin-1Interleukin-6Interleukin-6 Inhibitorsdensity functional theoryherbal compoundsinflammationinterleukin-1interleukin-6molecular dynamics.Rheumatoid arthritis

Identifiers

PMID40114577

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.