Preclinical Pharmacokinetic-Pharmacodynamic Modeling of Antibody Nanoconjugates for Breast Cancer Treatment.

Authors

Anne Rodallec, Cristina Vaghi, Joseph Ciccolini, Raphaelle Fanciullino, Sebastien Benzekry

Published in

CPT: pharmacometrics & systems pharmacology. Volume 15. Issue 10. Pages e70348.

Abstract

Over the past 5 years, the number of antibody-drug conjugates (ADCs) entering the oncology market has surged. By contrast, the clinical use of immunoliposomes-nanoparticles that also employ antibodies as targeting agents (i.e., antibody-nanoconjugates, ANCs)-has yet to receive approval. One possible reason is the limited understanding of the relationship between ANC exposure and efficacy. To address this gap, we developed a pharmacokinetic/pharmacodynamic (PK/PD) model for a docetaxel-trastuzumab ANC. PK and PD studies were conducted in MDA-MB-231 tumor-bearing mice, divided into three treatment groups: (i) control, (ii) free docetaxel + free trastuzumab, and (iii) docetaxel-trastuzumab ANCs. PK data were obtained by fluorescence imaging and captured systemic and tumor distribution of ANCs, while PD data were based on tumor growth measurements. The PK model was built using data from the ANC group, and the PD model was developed across all three treatment groups, with a leave-one-out cross validation strategy for predictive evaluation. Treatment schedule simulations were then performed. Using a population nonlinear mixed-effects (NLME) approach, the final PK model included a central and a tumor compartment, each with independent elimination rates. Tumor growth dynamics were best described by a reduced Gompertz model coupled with a Simeoni resistance model. This model demonstrated strong predictive performance. Using only data from the first two treatment cycles (4 time points) and Bayesian estimation in the test set, it accurately predicted systemic and tumor ANC concentrations across the subsequent four treatment cycles, with a relative error (RE) below 15%. Prediction precision was further improved (RE < 5%) when data from one additional treatment cycle were incorporated. Similar performance was achieved for PD data, with tumor volume predictions exhibiting RE < 15% and RE < 7.5% when implementing data from two and three treatment cycles, respectively. The model was then applied to simulate alternative treatment schedules. Interestingly, while systemic exposure remained comparable across regimens, every-5-day administration yielded the greatest tumor exposure compared to daily, weekly, and every-35-day dosing (mean ± SD tumor AUC: 55.4 ± 10.9 mg/kg day vs. 47.6 ± 9.38 mg/kg day, 51.4 ± 11.5 mg/kg day, and 44.6 ± 10.0 mg/kg day, respectively, one-way ANOVA, p < 0.001), highlighting the importance of dosing frequency. Overall, these results illustrate how PK/PD modeling can refine intra-tumor PK understanding and support preclinical development of innovative formulations such as ANCs.

PMID:
42861467
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.

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