ArticleBMC dermatology2018
Elucidating mechanistic insights into drug action for atopic dermatitis: a systems biology approach.
Article in BMC dermatology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026Review
- Causal inference of inflammatory proteins in infertility: a Mendelian randomization study.Frontiers in endocrinology · 2025Article
- Treatment With Schistosoma Japonicum Peptide SJMHE1 and SJMHE1-Loaded Hydrogel for the Mitigation of Psoriasis.Psoriasis (Auckland, N.Z.) · 2025Article
- Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council.The Journal of allergy and clinical immunology · 2019Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTopical Betamethasone (BM) and Pimecrolimus (PC) are widely used drugs in the treatment of atopic dermatitis (AD). Though the biomolecules and biological pathways affected by the drugs are known, the causal inter-relationships among these pathways in the context of skin is not available. We aim to derive this insight by using transcriptomic data of AD skin samples treated with BM and PC using systems biology approach.
methodsTranscriptomic datasets of 10 AD patients treated with Betamethasone and Pimecrolimus were obtained from GEO datasets. We used a novel computational platform, eSkIN ( www.persistent.com/eskin ), to perform pathway enrichment analysis for the given datasets. eSkIN consists of 35 skin specific pathways, thus allowing skin-centric analysis of transcriptomic data. Fisher's exact test was used to compute the significance of the pathway enrichment. The enriched pathways were further analyzed to gain mechanistic insights into the action of these drugs.
resultsOur analysis highlighted the molecular details of the mechanism of action of the drugs and corroborated the known facts about these drugs i.e. BM is more effective in triggering anti-inflammatory response but also causes more adverse effect on skin barrier than PC. In particular, eSkIN helped enunciate the biological pathways activated by these drugs to trigger anti-inflammatory response and its effect on skin barrier. BM suppresses pathways like TNF and TLRs, thus inhibiting NF-κB while PC targets inflammatory genes like IL13 and IL6 via known calcineurin-NFAT pathway. Furthermore, we show that the reduced skin barrier function by BM is due to the suppression of activators like AP1 transcription factors, CEBPs.
conclusionWe thus demonstrate the detailed mechanistic insight into drug action of AD using a novel computational approach.
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