ArticleBioactive materials2026
Machine learning-guided composite ionic liquid-based system for dual-drug delivery targeting redox homeostasis and STAT3-PI3K axis in psoriasis therapy.
Article in Bioactive materials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative analysis of supervised machine learning algorithms for transdermal drug delivery in brain disorders.Journal of computer-aided molecular design · 2026Article
- Mechanisms and Research Advances in Drug Delivery Systems for Psoriasis and Atopic Dermatitis.Clinical, cosmetic and investigational dermatology · 2026Review
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Authors and funding
8 authors.
Funding
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Abstract
Excessive accumulation of reactive oxygen and nitrogen species (RONS) exacerbates inflammatory responses and contributes to the progression of psoriasis. In particular, ROS activate the STAT3 pathway, inducing abnormal proliferation of keratinocytes and aggravating local inflammation. Moreover, interactions between macrophages and keratinocytes can further exacerbate disease progression. However, current therapeutic strategies have limited efficacy due to poor transdermal permeability and insufficient target specificity. To address these limitations, we have developed a machine learning (ML)-guided framework that integrates virtual screening, experimental validation, and mechanistic analysis into the design of transdermal ionic liquids (ILs). Using this approach, we successfully identified highly efficient transdermal ILs and developed a composite ionic liquids (CIL) delivery system capable of releasing H
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