Nan Zhang1, Mike Cheng1, Patrick Mitchell2, Carine Paturel4, Pascale Andre4, Kourtesis Panagiotis1, Mayukh Das1, Nassim Morsli3, Megan Gibbs1, Xuyang Song1
1AstraZeneca, Gaithersburg, MD, USA; 2AstraZeneca, Waltham, MA, USA; 3AstraZeneca, Cambridge, UK; 4Innate Pharma, 117 Avenue de Luminy, 13009 Marseille, France.
Objectives: Monalizumab is a potentially first-in-class humanized immunoglobulin 4 monoclonal antibody that targets Natural Killer Group 2A (NKG2A), resulting in suppression of inhibitory signaling by tumors on NK cells and a small subset of CD8+ T cells. This analysis was to develop a population pharmacokinetics (PK) model of monalizumab, and identify and quantify the influence of baseline and longitudinal patient and disease characteristics on PK.
Methods: Data from 369 patients receiving monalizumab at 22.5, 75, 225, or 750 mg Q2W and 750 mg Q4W in study D419NC00001 (NCT02671435, phase 1/2: monalizumab + durvalumab in all comers) and data from 74 patients receiving monalizumab at 0.4, 1, 2, 4, or 10 mg/kg, 750 mg Q2W, or 1500 mg Q4W in study IPH2201-203 (NCT02643550, phase 2: monalizumab + cetuximab in head and neck squamous cell carcinoma) were pooled for this PK analysis. Population PK modeling was performed using a non-linear mixed effects modeling approach in NONMEM (v. 7.3). A stepwise covariate modeling approach was used to explore the impact of clinical and disease characteristics including longitudinal biomarkers on PK. The final model was evaluated and validated by goodness-of-fit plots and using visual predictive check, respectively. Five hundred simulations were conducted to evaluate the impact of body weight on PK exposure of monalizumab in a population of 500 patients with a constant albumin level (the median level in the original dataset). Baseline body weights were resampled for the 500 patients using the distribution in the original dataset.
Results: In total, 2326 serum concentration data points were used for population PK analysis. Serum concentrations of monalizumab were best described by a two-compartment model with linear clearance. The typical values of central clearance (CL) and intercompartmental clearance (Q) were 0.263 and 0.453 L/day with a between-subject variability of 34.5% for CL and 0% (fixed) for Q, respectively. The central compartment volume of distribution (V1) and peripheral compartment volume of distribution (V2) were 3.9 and 2.49 L with a between-patient variability of 24.4% and 61%, respectively. CL and V1 both increased with increasing body weight. At the 5th and 95th percentiles of weight in the population (47 or 118 kg, respectively), CL was 82% and 149% of that at median body weight (70 kg). The volume of distribution at the 5th and 95th percentiles of weight was 85% and 140% of the volume of distribution at median body weight. The inclusion of time-varying albumin as a covariate on PK parameters improved the modeling fitting significantly (p-value < 0.001). CL and V1 decreased as the individual longitudinal albumin level increased and vice versa. When the albumin level changed from 2.7 to 4.6 g/dL, CL and V1 decreased from 385% to 71.6% and from 109% to 93.9% of the typical value, respectively. This finding of monalizumab PK is consistent with other checkpoint inhibitor drugs, e.g. durvalumab and ipilimumab 1,2. The decrease in CL with increasing albumin may be related to the decrease in nonspecific protein catabolic rates in cancer patients as their disease condition improved during treatment. PK simulations showed the body weight relationship was not clinically relevant, indicating no need for dose adjustment.
Conclusions: This is the first population PK model of monalizumab describing effects of longitudinal patient/disease characteristics. This quantitative approach supports the hypothesis that increase in body weight and albumin over time is associated with increase and decrease in monalizumab CL, consistent with other checkpoint inhibitor drugs. No dose adjustments were needed to account for the impact of covariate baseline body weight. The population PK model supports previous dose selection of 750 mg Q2W or equivalent dose in future Phase 2/3 clinical studies.
References:
[1] Baverel PG, Dubois VFS, Jin CY, Zheng Y, Song X, Jin X, Mukhopadhyay P, Gupta A, Dennis PA, Ben Y, Vicini P, Roskos L, Narwal R. Population Pharmacokinetics of Durvalumab in Cancer Patients and Association With Longitudinal Biomarkers of Disease Status. Clin Pharmacol Ther. 2018 Apr;103(4):631-642. doi: 10.1002/cpt.982. Epub 2018 Feb 2. PMID: 29243223; PMCID: PMC5887840.
[2] Sanghavi K, Zhang J, Zhao X, Feng Y, Statkevich P, Sheng J, Roy A, Vezina HE. Population Pharmacokinetics of Ipilimumab in Combination With Nivolumab in Patients With Advanced Solid Tumors. CPT Pharmacometrics Syst Pharmacol. 2020 Jan;9(1):29-39. doi: 10.1002/psp4.12477. Epub 2019 Dec 1. PMID: 31709718; PMCID: PMC6966186.
Reference: PAGE 29 (2021) Abstr 9711 [www.page-meeting.org/?abstract=9711]
Poster: Drug/Disease Modelling - Oncology