Ke Szeto 1, Yuchen Wang 1, Vivek Purohit 1, Jess Wojciechowski 1, Leandro Santos 1
1 Incyte Corporation (Wilmington , United States)
Objectives:
Povorcitinib (INCB054707) is a novel, orally administered, highly selective Janus kinase (JAK)1 inhibitor currently in phase 3 clinical trials across several inflammatory diseases [1]. The primary objective of this analysis was to quantitatively characterize the in vivo functional selectivity of povorcitinib relative to marketed JAK inhibitors using model-informed pharmacokinetic (PK) contextualization approaches. Specifically, we aimed to (i) compare steady-state exposure profiles across compounds and disease indications at clinically relevant maintenance doses, (ii) evaluate JAK1 and JAK2 target coverage using time above half-maximal inhibitory concentration (ICâ‚…â‚€), daily inhibition, and ICâ‚…â‚€/maximum observed concentration (Cmax) exposure margins, (iii) assess the impact of elimination half-life and peak to trough fluctuation on sustained versus intermittent pathway inhibition, and (iv) determine whether PK attributes contribute to differential JAK1 selectivity in addition to in vitro potency. Based on these analyses, we aim to establish a quantitative framework linking exposure dynamics to functional selectivity within the JAK inhibitor class.
Methods:
Population PK (popPK) simulations were conducted for povorcitinib and marketed JAK inhibitors (upadacitinib [2-5], abrocitinib [6], baricitinib [7,8], tofacitinib [9-12]) using published or internally developed models, across select disease indications as well as healthy volunteers. Simulations were implemented using mrgsolve and rxode2 based on published or internally developed popPK models for each compound. For povorcitinib, simulations utilized the established two-compartment model with transit absorption, developed from pooled phase 1–3 data including healthy volunteers and patients with hidradenitis suppurativa (HS), prurigo nodularis (PN), and vitiligo. Simulated doses included 45 mg and 75 mg once daily (qd) for HS and PN, and 30 mg qd for vitiligo. For the other JAK inhibitors, simulations were based on indication-specific models available in the literature.
For each dose indication scenario, 1000 virtual participants were simulated under steady-state conditions. Model covariates were fixed at reference values except for population designation. Steady-state concentration-time profiles over a 24-hour dosing interval were generated for each individual. The simulation results were aggregated across 1000 virtual participants, and outcomes were reported as mean (standard deviation) or geometric mean (% coefficient of variation) as appropriate. Primary PK metrics included steady-state area under the concentration-time curve (AUCss), maximum concentration (Cmax,ss), minimum concentration (Cmin,ss), and average concentration (Cavg,ss). Peak-to-trough ratios were also derived to characterize exposure fluctuation. Functional JAK1 and JAK2 selectivity was assessed and contextualized using whole-blood ICâ‚…â‚€ values for JAK1 and JAK2 inhibition. Time above ICâ‚…â‚€, daily inhibition percentages, and ICâ‚…â‚€/Cmax ratios were calculated.
Results:
At 75 mg qd, povorcitinib showed sustained concentrations above the JAK1 IC50 value for 23.3 hours (97% of daily dosing interval) with 0 hours above the JAK2 IC₅₀. The JAK2 IC₅₀/Cmax ratio was 6.58, corresponding to low daily JAK2 inhibition (~11%). In contrast, upadacitinib (JAK1 inhibitor at 30 mg qd) demonstrated 10.8-hour JAK1 coverage and 5.9 hours above JAK2 IC₅₀, with JAK2 IC₅₀/Cmax <1 and daily JAK2 inhibition ~43%. Abrocitinib (JAK1 inhibitor at 200 mg qd) showed 8.4-hour JAK1 inhibition and intermittent JAK2 engagement. However, because metabolites and their potential pharmacologic activity were not explicitly incorporated into the simulation framework, overall JAK1 and JAK2 target coverage may be underestimated. The nonselective JAK inhibitors baricitinib (4 mg qd) and tofacitinib (10 mg twice daily) exhibited prolonged JAK2 engagement (19.2 and 3.7 hours, respectively). Povorcitinib displayed the longest half-life (~44 hours) and lowest peak-to-trough ratio (1.62), whereas the other JAK inhibitors showed shorter half-lives (3–28 hours) and substantially greater exposure fluctuation (peak-to-trough ratios 8–126).
Conclusions:
The PK/pharmacologic contextualization modeling highlights povorcitinib as a best-in-class JAK1 inhibitor considering its differentiated PK properties, as well as the high JAK1-over-JAK2 selectivity, yielding sustained and clinically relevant pathway modulation. These data support functional selectivity in vivo and illustrate how exposure dynamics and in vitro selectivity determine class differentiation among JAK inhibitors. This integrated PK profile can be used to assess therapeutic potential across immune-mediated inflammatory diseases requiring sustained selective inhibition of JAK1 pathway.
References:
1. Wass B, Fenix A, Zolotarjova N, et al. Comparative analyses highlight povorcitinib (INCB054707) as a potential best-in-class selective JAK1 inhibitor. Submitted for publication
2. Bhatnagar S, Schlachter L, Eckert D, et al. Pharmacokinetics and exposure-response analyses to support dose selection of upadacitinib in Crohn's disease. Clin Pharmacol Ther. 2024;116:1240-1251
3. Ismail M, Doelger E, Eckert D, et al. Population pharmacokinetic and exposure-response modelling to inform upadacitinib dose selection in adolescent and adult patients with atopic dermatitis. Br J Clin Pharmacol. 2023;89:3139-3151
4. Klunder B, Mittapalli RK, Mohamed MF, et al. Population pharmacokinetics of upadacitinib using the immediate-release and extended-release formulations in healthy subjects and subjects with rheumatoid arthritis: analyses of phase I-III clinical trials. Clin Pharmacokinet. 2019;58:1045-1058
5. Ponce-Bobadilla AV, Stodtmann S, Eckert D, et al. Upadacitinib population pharmacokinetics and exposure-response relationships in ulcerative colitis patients. Clin Pharmacokinet. 2023;62:101-112
6. Wojciechowski J, Malhotra BK, Wang X, et al. Population pharmacokinetics of abrocitinib in healthy individuals and patients with psoriasis or atopic dermatitis. Clin Pharmacokinet. 2022;61:709-723
7. Decker RL, Ernest CS, 2nd, Radtke DB, et al. A population pharmacokinetic and exposure-response analysis for baricitinib in pediatric patients with atopic dermatitis. Clin Pharmacokinet. 2026;65:119-131
8. US Food and Drug Administration, Center for Drug Evaluation and Research. Clinical Pharmacology and Biopharmaceutics Review: NDA 207924Orig1s000. 2018. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2018/207924Orig1s000ClinPharmR.pdf Accessed February 23, 2026.
9. Tsuchiwata S, Suzuki A, Wang Q, et al. Population pharmacokinetics of tofacitinib in patients with active ankylosing spondylitis. Int J Clin Pharmacol Ther. 2026;64:57-65
10. US Food and Drug Administration, Center for Drug Evaluation and Research. Clinical Pharmacology and Biopharmaceutics Reviews. Application number: 203214Orig1s000. 2012. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2012/203214Orig1s000ClinPharmR.pdf Accessed February 23, 2026.
11. Vong C, Martin SW, Deng C, et al. Clin Pharmacol Drug Dev. 2021;10:229-240
12. Xie R, Deng C, Wang Q, et al.
Int J Clin Pharmacol Ther. 2019;57:464-473
Reference: PAGE 34 (2026) Abstr 11972 [www.page-meeting.org/?abstract=11972]
Poster: Drug/Disease Modelling - Safety