1Crown Bioscience, Taicang, China,2Crown Bioscience, Inc., San Diego, CA
摘要 Abstract
Introduction Accurate human pharmacokinetic (PK) prediction for antibody-drug conjugates (ADCs) remains a pivotal challenge in early discovery and preclinical stages. Wild-type (WT) mouse models poorly predict human PK due to species-specific FcRn interactions. While hFcRn transgenic models improve mAb PK prediction, their value for complex ADCs requires further validation. This study evaluates hFcRn and HSA/hFcRn transgenic mice using marketed ADCs to establish their role in translational DMPK assessment.
Methods Four ADCs (T-DXd, T-DM1, SG, EV) were administered intravenously (10 mg/kg) to C57BL/6 WT, hFcRn, and HSA/hFcRn mice. Serial blood samples were collected over 28 days. Total antibody and ADC concentrations were measured by ELISA, free payloads by LC-MS/MS. PK parameters were derived via non-compartmental analysis.
Results WT mice consistently overestimated ADC half-lives, while hFcRn transgenic models showed human-relevant values. HSA/hFcRn mouse half-lives correlated strongly with human (r²=0.95, p<0.05) and NHP (r²=0.98, p<0.01). Please find the comparison of PK parameters in the table 1.
Furthermore, the platform successfully differentiated the stability characteristics between ADCs with stable (T-DXd) and more labile (EV) linkers, providing insights into ADC structure complexity and PK relationships.
Conclusion This study demonstrates that hFcRn transgenic mouse model accurately recapitulates human-relevant PK profiles that directly address a major limitation of conventional WT mouse models. The implementation of this platform enables more reliable human PK predictions and informs FIH trial design, and also provides a strategic approach to de-risking ADC development through data-driven candidate selection, reducing early discovery and late-stage dependency on NHP studies, and accelerating the translation of promising ADC therapeutics into clinical evaluation.
利益披露 Disclosure
X. Feng, None..
K. Gong, None..
W. Wang, None..
X. Ding, None..
X. Tu, None..
L. Yu, None.