(S-084) Population pharmacokinetic modeling of anti-B7H4 antibody-drug conjugate (SGN-B7H4V) in patients with advanced solid tumors from phase 1 study
Sunday, October 19, 2025
7:00 AM - 5:00 PM MDT
Location: Colorado A
Hossam Kadry – Pharmacometrics and System Pharmacology (PSP) – Pfizer; John Harrold – Pharmacometrics and System Pharmacology (PSP) – Pfizer; Ping Xu – Biostatistics – Pfizer; JoAl Mayor – Global Clinical Development – Pfizer; Faye Zhang – Clinical Pharmacology – Pfizer
Pharmacometrician Pfizer Inc. Marysville, Washington, United States
Disclosure(s):
Hossam Kadry: No financial relationships to disclose
Objectives: B7-H4 is an immune checkpoint ligand expressed at low levels in normal tissue and upregulated in solid tumors, including breast, ovarian, and endometrial cancers. SGN-B7H4V is an antibody-drug conjugate (ADC) comprising a B7-H4-directed monoclonal antibody conjugated to monomethyl auristatin (MMAE) via a protease-cleavable linker that has been evaluated in a Phase 1 study. A population pharmacokinetic (PK) model of SGN-B7H4V was developed to characterize antibody-conjugated MMAE (acMMAE) PK data and to evaluate the impact of baseline covariates on acMMAE exposure.
Methods: The PK data from the Phase 1 study (NCT05194072) were used in the PK model development. This analysis included a total of 243 patients with refractory/relapsed solid tumors who received SGN-B7H4V at doses ranging from 0.75-2.0 mg/kg across 2 different schedules, 2Q3W and Q2W. Serum concentrations for acMMAE (n=5105) were characterized using nonlinear mixed-effects modeling approach. Clinically relevant patient specific baseline characteristics including body weight (WT), body mass index (BMI), body surface area (BSA), age, and baseline tumor volume were explored to explain the interindividual variability of key parameters, such as clearance (CL) and plasma volume (Vc). Post-hoc estimated PK parameters were used to simulate exposure based on AiBW (Adjusted-ideal Body Weight).
Results: A two-compartment model with linear elimination reasonably described acMMAE concentrations. All parameters were estimated with good precision and the final model included the effect of WT on both CL and Vc. Adding WT, as a significant covariate, reduced interindividual variability (IIV) to 21.3% for CL and 14.1% for Vc. Simulated exposure based on AiBW showed a great influence in minimizing PK variability across wide range distribution of BW and BMI groups.
Conclusions: The final popPK model was able to reasonably characterize acMMAE PK data across different subject,s with WT being the significant covariate. AiBW-based dosing may be considered to minimize PK variability across a range of BW and BMI.
