RESUMEN
BACKGROUND: INCB018424 is a novel, potent Janus kinase (JAK)1/JAK2 inhibitor that blocks signal transduction of multiple proinflammatory cytokines. OBJECTIVES: To evaluate the safety, tolerability, pharmacokinetics, pharmacodynamics and preliminary efficacy of topical INCB018424 phosphate cream in patients with plaque psoriasis. METHODS: Topical INCB018424 phosphate 1·0% or 1·5% cream was applied once daily (QD) or twice daily (BID) for 4 weeks to 2-20% body surface area in five sequential cohorts of five patients aged 18-65 years. Target lesions were scored on a scale of 0-4 for erythema, scaling and thickness. Additionally, the overall disease activity in each patient was measured using Physician's Global Assessment. INCB018424 concentrations were measured in plasma, and cytokine stimulated phosphorylated signal transducer and activator of transcription 3 phosphorylation (pSTAT3) levels in peripheral blood cells were evaluated. Pretreatment and post-treatment skin biopsies were compared with healthy skin, including evaluation of histopathology, immunohistochemistry and mRNA expression. RESULTS: Treatment with INCB018424 phosphate cream either 1·0% QD or 1·5% BID resulted in improvements in lesion scores. No significant inhibition of pSTAT3 in peripheral blood cells was observed following topical application, consistent with the generally low steady-state plasma concentrations of INCB018424 measured. Transcriptional markers of immune cell lineage/activation in lesional skin were reduced by topical INCB018424, with correlations observed between clinical improvement and decreases in markers of T helper 17 lymphocyte activation, dendritic-cell activation and epidermal hyperplasia. INCB018424 treatment reduced epidermal hyperplasia and dermal inflammation in most patient samples, with reductions in CD3, CD11c, Ki67 and keratin 16 observed by immunohistochemical analysis. CONCLUSIONS: Topical INCB018424 dosed for 28 days QD or BID is pharmacologically active in patients with active psoriasis and modulates proinflammatory cytokines in the pathogenesis of psoriatic lesions.
Asunto(s)
Fármacos Dermatológicos/administración & dosificación , Inhibidores de Proteínas Quinasas/administración & dosificación , Psoriasis/tratamiento farmacológico , Pirazoles/administración & dosificación , Administración Cutánea , Adolescente , Adulto , Anciano , Biomarcadores/metabolismo , Citocinas/metabolismo , Fármacos Dermatológicos/efectos adversos , Fármacos Dermatológicos/farmacocinética , Relación Dosis-Respuesta a Droga , Regulación hacia Abajo/efectos de los fármacos , Femenino , Humanos , Janus Quinasa 1/antagonistas & inhibidores , Janus Quinasa 2/antagonistas & inhibidores , Masculino , Persona de Mediana Edad , Nitrilos , Pomadas , Inhibidores de Proteínas Quinasas/efectos adversos , Inhibidores de Proteínas Quinasas/farmacocinética , Pirazoles/efectos adversos , Pirazoles/farmacocinética , Pirimidinas , Factor de Transcripción STAT3/metabolismo , Células TH1/metabolismo , Células Th17/metabolismo , Activación Transcripcional/efectos de los fármacos , Resultado del Tratamiento , Adulto JovenRESUMEN
Physiologically based pharmacokinetic modeling was applied to characterize the potential drug-drug interactions for ruxolitinib. A ruxolitinib physiologically based pharmacokinetic model was constructed using all baseline PK data in healthy subjects, and verified by retrospective predictions of observed drug-drug interactions with rifampin (a potent CYP3A4 inducer), ketoconazole (a potent CYP3A4 reversible inhibitor) and erythromycin (a moderate time-dependent inhibitor of CYP3A4). The model prospectively predicts that 100-200 mg daily dose of fluconazole, a dual inhibitor of CYP3A4 and 2C9, would increase ruxolitinib plasma concentration area under the curve by â¼two-fold, and that as a perpetrator, ruxolitinib is highly unlikely to have any discernible effect on digoxin, a sensitive P-glycoprotein substrate. The analysis described here illustrates the capability of physiologically based pharmacokinetic modeling to predict drug-drug interactions involving several commonly encountered interaction mechanisms and makes the case for routine use of model-based prediction for clinical drug-drug interactions. A model verification checklist was explored to harmonize the methodology and use of physiologically based pharmacokinetic modeling.