Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition

The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h-6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.

Quantitative Evaluation of the Effect of Antigen Expression Level on Antibody-Drug Conjugate Exposure in Solid Tumor

Antibody-drug conjugates (ADCs) rely on high expression of target antigens on cancer cells to effectively enter the cell and release a cytotoxic payload. Previous studies have shown that ADC efficacy is not always tied to antigen expression. However, our recent in vitro study suggests a linear relationship between antigen expression and the intracellular levels of the ADC payload. In this study, we have explored the relationship between antigen expression and intratumoral ADC exposure in vivo. Using trastuzumab-vc-MMAE (T-vc-MMAE) and four cell lines with varying expression of human epithelial growth factor receptor 2 (HER2), the pharmacokinetics of total trastuzumab, released ("free") MMAE, and total MMAE were evaluated in a tumor xenograft model. Nude mice were implanted with tumors originating from BT-474, MDA-MB-453, MCF-7, and MDA-MB-468 cell lines and dosed with 10 mg/kg or 1 mg/kg of ADC. Observed data were mathematically characterized using a mechanism-based PK model. A strong positive correlation was observed between antigen expression levels and free/total MMAE exposure (R² ≥ 0.91) (total MMAE being the sum of released and conjugated MMAE) within the tumor, but not for total trastuzumab exposure. The PK model was able to recapitulate plasma PK through simulation; however, the tumor PK was overpredicted or underpredicted in some cases potentially due to differences in tumor vasculature or extracellular matrix conditions. Our results indicate a linear relationship between antigen expression and tumor exposure of free/total ADC payload in vivo, validating our previous finding in vitro, while also revealing the need to understand complex physiology of the tumor to predict tumor PK of ADC and its components. Our findings also support the concept of antigen expression screening in patients for targeted therapies like ADCs to achieve the maximum therapeutic benefit of the treatment.

A Cell-Level Systems PK-PD Model to Characterize In Vivo Efficacy of ADCs

Here, we have presented the development of a systems pharmacokinetics-pharmacodynamics (PK-PD) model for antibody-drug conjugates (ADCs), which uses intracellular target occupancy to drive in-vivo efficacy. The model is built based on PK and efficacy data generated using Trastuzumab-Valine-Citrulline-Monomethyl Auristatin E (T-vc-MMAE) ADC in N87 (high-HER2) and GFP-MCF7 (low-HER2) tumor bearing mice. It was observed that plasma PK of all ADC analytes was similar between the two tumor models; however, total trastuzumab, unconjugated MMAE, and total MMAE exposures were >10-fold, ~1.6-fold, and ~1.8-fold higher in N87 tumors. In addition, a prolonged retention of MMAE was observed within the tumors of both the mouse models, suggesting intracellular binding of MMAE to tubulin. A systems PK model, developed by integrating single-cell PK model with tumor distribution model, was able to capture all in vivo PK data reasonably well. Intracellular occupancy of tubulin predicted by the PK model was used to drive the efficacy of ADC using a novel PK-PD model. It was found that the same set of PD parameters was able to capture MMAE induced killing of GFP-MCF7 and N87 cells in vivo. These observations highlight the benefit of adopting a systems approach for ADC and provide a robust and predictive framework for successful clinical translation of ADCs.

Integrating Dynamic Positron Emission Tomography and Conventional Pharmacokinetic Studies to Delineate Plasma and Tumor Pharmacokinetics of FAU, a Prodrug Bioactivated by Thymidylate Synthase

FAU, a pyrimidine nucleotide analogue, is a prodrug bioactivated by intracellular thymidylate synthase to form FMAU, which is incorporated into DNA, causing cell death. This study presents a model-based approach to integrating dynamic positron emission tomography (PET) and conventional plasma pharmacokinetic studies to characterize the plasma and tissue pharmacokinetics of FAU and FMAU. Twelve cancer patients were enrolled into a phase 1 study, where conventional plasma pharmacokinetic evaluation of therapeutic FAU (50-1600 mg/m² ) and dynamic PET assessment of ¹⁸ F-FAU were performed. A parent-metabolite population pharmacokinetic model was developed to simultaneously fit PET-derived tissue data and conventional plasma pharmacokinetic data. The developed model enabled separation of PET-derived total tissue concentrations into the parent drug and metabolite components. The model provides quantitative, mechanistic insights into the bioactivation of FAU and retention of FMAU in normal and tumor tissues and has potential utility to predict tumor responsiveness to FAU treatment.

Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1)

A mathematical model capable of accurately characterizing intracellular disposition of ADCs is essential for a priori predicting unconjugated drug concentrations inside the tumor. Towards this goal, the objectives of this manuscript were to: (1) evolve previously published cellular disposition model of ADC with more intracellular details to characterize the disposition of T-DM1 in different HER2 expressing cell lines, (2) integrate the improved cellular model with the ADC tumor disposition model to a priori predict DM1 concentrations in a preclinical tumor model, and (3) identify prominent pathways and sensitive parameters associated with intracellular activation of ADCs. The cellular disposition model was augmented by incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Different biomeasures and chemomeasures for T-DM1, quantified in the companion manuscript, were incorporated into the modified model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with the tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Pathway analysis suggested different contribution of antigen-mediated and passive diffusion pathways for intracellular unconjugated drug exposure between in vitro and in vivo systems. Global and local sensitivity analyses revealed that non-specific deconjugation and passive diffusion of the drug across tumor cell membrane are key parameters for drug exposure inside a cell. Finally, a systems pharmacokinetic model for intracellular processing of ADCs has been proposed to highlight our current understanding about the determinants of ADC activation inside a cell.