2023 - A Coruņa - Spain

PAGE 2023: Drug/Disease Modelling - Oncology
Huub Jan Kleijn

Population Pharmacokinetic Analysis of PD-L1 Checkpoint Inhibitor Cosibelimab in Subjects with Advanced Cancers

Nele Plock (1), Huub Jan Kleijn (1), Lauren Neighbours (2), James Oliviero (2)

(1) Certara, Princeton, NJ, USA, (2) Checkpoint Therapeutics, Inc, MA, USA

Introduction/Objectives: Cosibelimab is a high-affinity, fully human monoclonal antibody (mAb) of immunoglobulin G1 subtype that directly binds to PD-L1. PD-L1 is an immune-inhibitory checkpoint molecule that may be expressed by cancer cells, thereby evading the body’s immune response. Cosibelimab, which is currently in clinical development for the treatment of subjects with advanced cutaneous squamous cell carcinoma (cSCC) and other cancers, could prevent PD-1/PD-L1 binding and reactivate the anti-tumor immune response. Additionally, cosibelimab has a functional fragment crystallizable domain capable of inducing antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity against tumor cells. A population pharmacokinetic (PK) analysis was performed to determine the impact of relevant intrinsic and extrinsic factors on cosibelimab exposure.

Methods: Model development (using NONMEM version 7.3) was based on data from a Phase 1, open-label, multicenter, multiregional, dose-escalation and cohort-expansion study of cosibelimab administered intravenously (IV) to subjects with advanced cancer [1]. Subjects received cosibelimab in fixed doses of 200 mg, 400 mg, or 800 mg dosed once every two weeks (Q2W), or 1200 mg dosed once every three weeks (Q3W) as an IV infusion over 60 minutes. Dense PK sampling was performed on day one of cycle one, followed by sparse sampling at later time points and cycles. After model development, simulations were performed to assess the impact of clinically relevant covariates on cosibelimab exposure parameters at steady state.

Results: The analysis was based on 2527 evaluable samples from 206 subjects. The 200 mg Q2W and 400 mg Q2W regimens were administered to one subject each, while 169 and 35 subjects provided data for the 800 mg Q2W and 1200 mg Q3W regimens, respectively. Of all models tested, a two-compartment model with linear elimination provided the best fit to the data. Baseline body weight was directly incorporated as a covariate on all clearance (CL) and volume parameters during base model development, with estimated allometric exponents. The final model included effects of albumin, target lesion diameter, and race on CL as well as an effect of sex on central volume, along with allometric effects of body weight on CL and volume parameters. Cosibelimab has a CL of 0.238 L/day. Central volume of distribution (Vc) and peripheral volume of distribution (Vp) were estimated with values of 3.58 L and 2.31 L. Between-subject variability was included on CL as well as central and peripheral volumes. Using individual post hoc parameter estimates, the population PK model predicted a median half-life of 17.4 days. The impact of the covariates on area under the curve (AUC) was mostly small to moderate (<25%). Low body weight increased AUC by 33.1%, while Asians had 27% lower AUC as compared to non-Asians. Cmin was somewhat more affected by covariates as compared to AUC, while Cmax was least affected. Average cosibelimab concentrations were very comparable between 800 mg Q2W and 1200 mg Q3W dosing regimens. A 27% higher Cmax and 15% lower Cmin for 1200 mg Q3W was simulated as compared to 800 mg Q2W. PK parameters were similar between all tumor types.

Conclusions: Cosibelimab PK were well characterized using a linear 2-compartment model structure. All identified covariates are well established to impact the PK of other mAbs in oncology. Based on an exposure-response analysis performed on data from the same trial, the clinical impact of the identified covariate relationships as well as the clinical relevance of PK differences between the 800 mg Q2W and 1200 mg Q3W dosing regimens are considered negligible.

[1] ClinicalTrials.gov Identifier: NCT03212404

Reference: PAGE 31 (2023) Abstr 10300 [www.page-meeting.org/?abstract=10300]
Poster: Drug/Disease Modelling - Oncology
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