Cytokines that exert their biological effect by activating the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signalling pathway play a central role in inflammatory processes demonstrated by the clinical efficacy of JAK inhibitors in several chronic inflammatory diseases, including psoriasis.
With the purpose to support the development of a panJAK inhibitor for topical treatment of inflammatory skin diseases, we correlated the response of a proximal target engagement biomarker to distal inflammatory endpoints in in vivo pharmacology studies using the disease-relevant psoriasis xenograft mouse model. Briefly, skin keratome biopsies were obtained from active lesions of untreated patients with psoriasis vulgaris and grafted onto immune compromised mice. Target (JAK) activity and inflammatory endpoints were assessed in the psoriasis skin by the level of STAT3 phosphorylation and epidermal thickness and expression of keratin-16, respectively.
We used this model to test the efficacy of selective panJAK inhibitors derived from different chemical series. Engrafted skin was topically treated with test compounds twice daily for either 2.5 days (“mechanistic model”) or 28 days (“efficacy model”) to investigate treatment effect on target activity and downstream inflammatory processes, respectively. Interestingly, compared to vehicle-treated controls, significant inhibition of target activity was observed for some but not all tested compounds, which were largely equipotent, indicating that other factors, such as skin penetration, were affecting target engagement. Further, only the compounds with significant effect on target activity were found to have significant anti-inflammatory effect in the chronic inflammatory efficacy model. The results clearly indicate a direct translation between the short mechanistic model and the longer efficacy model in addressing drug responses.
In summary, we have established a short and predictive mechanistic mouse model to address modulation of JAK activity. This model has enabled us to efficiently identify and select which JAK inhibitors to progress for further development.