Jasia King 1, Erlend Johannessen Egeland 1, Chandrali Bhattacharya 2, Johanna Melin 1, Joanna Parkinson 1
1 Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca (, Sweden), 2 Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca (, USA )
Objectives: AZD4144 is a small‑molecule that inhibits the NLRP3 inflammasome, which is currently in clinical development for the treatment of acute kidney injury (AKI). AKI is a serious disease triggered by multiple clinical insults such as sepsis, ischemia–reperfusion injury, nephrotoxicity, dehydration, and urinary tract obstruction [1,2]. Targeting NLRP3 provides a mechanistic strategy to mitigate AKI‑associated pathobiology and improve clinical outcomes by lowering inflammation biomarkers [3]. In the current analysis, a population pharmacokinetic (popPK) model was developed based on data from single and repeated dosing of AZD4144 (IV infusion) to healthy participants. The model was used to predict drug exposure in participants with AKI to support dose selection for Phase 2a.
Methods: PK data collected in Phase 1 study in healthy volunteers were used in this assessment, A pooled dataset following single dosing (50, 125 and 300 mg) and repeated dosing (30, 90 and 200 mg) of AZD4144 as an IV infusion was analysed using nonlinear mixed‑effects modelling (NONMEM 7.6). Each dose was administered as a 1‑hour infusion on Day 1; for multiple ascending dose (MAD) cohorts daily dosing restarted on Days 4–12. PK sampling was done at following timepoints: pre-infusion, 0.5, 1 (end-of-infusion), 1.25, 1.5, 2, 3, 4, 5, 6, 8, 12, 16 h, 24, 36, 38, 58 and 72 hours post start of infusion for the single ascending dose (SAD) cohorts, and on day 1-3 and day 10-12 at pre-infusion, 20 min, 40 min,1 (end-of-infusion), 1.5, 2, 2.5, 3, 4, 5, 6, 8,10, 12, 18, 24, 30, 36, 40, and 48 h post-dose and pre-infusion samples were taken from day 5-11 for the MAD cohorts. Various structural models were assessed and compared by considering objective function changes, goodness-of-fit diagnosis, visual predictive checks (VPCs) stratified by dose and regimen. The data included in the model development included only healthy participants, therefore the impact of renal impairment on AZD4144 exposure could not be estimated from the available data. To account for the expected impact of renal function, an empirical factor was included in the model, where it was assumed that renal impairment affects clearance (CL) and that CL and eGFR have a linear relationship. A sensitivity analysis was performed on the empirical factor for renal impairment with the range of 1 (no effect) to 2-fold decreases in participants with renal impairment compared to healthy participants.
Results: A two‑compartment model with linear clearance most accurately described AZD4144 PK following single and repeated intravenous administration in healthy volunteers. CL and Central volume of distribution (VC) were estimated to be the following parameters (RSE%), CL: 4.25 L/h (3.6%), VC: 35.0 L (1.3%), Intercompartment Clearance (Q): 9.21 L/h (4.3%), Peripheral Volume of distribution (VP): 61.3 L (1.0%). Between subject variability (BSV) was included for CL and VC. The model was then used to simulate AZD4144 exposure in participants with various levels of renal impairment using the empirical factor to reduce the CL in renal impairment.
Conclusions: The popPK model was successfully developed for AZD4144, which provided a quantitative understanding of AZD4144’s pharmacokinetics in healthy volunteers. The model was used for simulating exposure in participants with renal impairment to understand the effect of reduced eGFR on exposure and the simulations that accounted for renal impairment provided an initial framework for predicting drug exposure in AKI populations. The model was subsequently updated with clinical renal impairment data to guide dose selection for Phase 2a study in participants with AKI.
References:
1. Kellum JA, Wen X, de Caestecker MP, Hukriede NA. Sepsis-associated acute kidney injury: a problem deserving of new solutions. Nephron. 2019;143(3):174-178. doi:10.1159/000500167
2. He FF, Wang YM, Chen YY, Huang W, Li ZQ, Zhang C. Sepsis-induced AKI: from pathogenesis to therapeutic approaches. Front Pharmacol. 2022;13:981578. doi:10.3389/fphar.2022.981578
3. Shi, X., Tan, S., & Tan, S. 2021. NLRP3 inflammasome in sepsis (Review). Molecular Medicine Reports, 24, 514. https://doi.org/10.3892/mmr.2021.12153
Reference: PAGE 34 (2026) Abstr 12001 [www.page-meeting.org/?abstract=12001]
Poster: Drug/Disease Modelling - Other Topics