A Novel Chronic Psoriasis Mouse Model via Optimized Imiquimod Dosing and Machine Learning Evaluation

Imiquimod (IMQ)-induced psoriasis-like mouse models are widely used for psoriasis research, but existing methods fail to sustain disease manifestation over time. This study explores the effect of different IMQ dosing frequencies on maintaining psoriasis symptoms in mice.

We compared a general IMQ model (General Model) with models that used spaced dosing (D-D Model) or 5-6 doses per week (3D-D Model) over a 28-day duration. Each experimental group consisted of eight mice (n=8) to ensure statistical significance. Both the D-D and 3D-D models maintained classic pathological features of psoriasis, including immune cell accumulation in skin and sustained levels of psoriasis-related inflammatory factors in the blood, compared to the control and General Model groups. Transcriptomic analysis revealed that D-D Model and 3D-D models mice exhibited more severe psoriasis-like lesions and significantly increased expression of IL-17 and IL-23 signaling genes (IL-17A, IL-17F, S100A9) compared to the General Model. Furthermore, adjusted dosing frequencies influenced the metabolic profile, with higher regulation of TRP channels and 2-oxocarboxylic acid metabolism in the skin of D-D mice. Subsequent, Identification and validation of a conserved psoriasis biomarker signature via machine learning and cross-species analysis.

Adjusting the dosing frequency of conventional imiquimod-induced psoriasis-like mouse models to alternate-day administration (D-D) or three days on followed by one day off (3D-D) maintained long-term psoriasis symptoms. enhancing IL-17/IL-23 signaling pathways. This modification resulted in a model exhibiting biological characteristics more closely resembling those in humans, thereby providing a more clinically relevant model for chronic psoriasis. Despite these advantages, the current model has not yet fully recapitulated the complex seasonal and cyclical nature of clinical psoriasis.