Physiologically Based Modeling of the Pharmacokinetics of "Catch-and-Release" Anti-Carcinoembryonic Antigen Monoclonal Antibodies in Colorectal Cancer Xenograft Mouse Models

Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art

Colorectal cancer remains a major cause of cancer death and morbidity worldwide. Surgery is a major treatment modality for primary and, increasingly, secondary curative therapy. However, with more patients being diagnosed with early stage and premalignant disease manifesting as large polyps, greater accuracy in diagnostic and therapeutic precision is needed right from the time of first endoscopic encounter. Rapid advancements in the field of artificial intelligence (AI), coupled with widespread availability of near infrared imaging (currently based around indocyanine green (ICG)) can enable colonoscopic tissue classification and prognostic stratification for significant polyps, in a similar manner to contemporary dynamic radiological perfusion imaging but with the advantage of being able to do so directly within interventional procedural time frames. It can provide an explainable method for immediate digital biopsies that could guide or even replace traditional forceps biopsies and provide guidance re margins (both areas where current practice is only approximately 80% accurate prior to definitive excision). Here, we discuss the concept and practice of AI enhanced ICG perfusion analysis for rectal cancer surgery while highlighting recent and essential near-future advancements. These include breakthrough developments in computer vision and time series analysis that allow for real-time quantification and classification of fluorescent perfusion signals of rectal cancer tissue intraoperatively that accurately distinguish between normal, benign, and malignant tissues in situ endoscopically, which are now undergoing international prospective validation (the Horizon Europe CLASSICA study). Next stage advancements may include detailed digital characterisation of small rectal malignancy based on intraoperative assessment of specific intratumoral fluorescent signal pattern. This could include T staging and intratumoral molecular process profiling (e.g. regarding angiogenesis, differentiation, inflammatory component, and tumour to stroma ratio) with the potential to accurately predict the microscopic local response to nonsurgical treatment enabling personalised therapy via decision support tools. Such advancements are also applicable to the next generation fluorophores and imaging agents currently emerging from clinical trials. In addition, by providing an understandable, applicable method for detailed tissue characterisation visually, such technology paves the way for acceptance of other AI methodology during surgery including, potentially, deep learning methods based on whole screen/video detailing.

Application of physiologically-based pharmacokinetic models for therapeutic proteins and other novel modalities

The past two decades have seen diversification of drug development pipelines and approvals from traditional small molecule therapies to alternative modalities including monoclonal antibodies, engineered proteins, antibody drug conjugates (ADCs), oligonucleotides and gene therapies. At the same time, physiologically-based pharmacokinetic (PBPK) models for small molecules have seen increased industry and regulatory acceptance.This review focusses on the current status of the application of PBPK models to these newer modalities and give a perspective on the successes, challenges and future directions of this field.There is greatest experience in the development of PBPK models for therapeutic proteins, and PBPK models for ADCs benefit from prior experience for both therapeutic proteins and small molecules. For other modalities, the application of PBPK models is in its infancy.Challenges are discussed and a common theme is lack of availability of physiological and experimental data to characterise systems and drug parameters to enable a priori prediction of pharmacokinetics. Furthermore, sufficient clinical data are required to build confidence in developed models.The PBPK modelling approach provides a quantitative framework for integrating knowledge and data from multiple sources and can be built on as more data becomes available.

Cell Penetrating Peptides Conjugated to Anti-Carcinoembryonic Antigen "Catch-and-Release" Monoclonal Antibodies Alter Plasma and Tissue Pharmacokinetics in Colorectal Cancer Xenograft Mice

Cell penetrating peptides conjugated to delivery vehicles, such as nanoparticles or antibodies, can enhance the cytosolic delivery of macromolecules. The present study examines the effects of conjugation to cell penetrating and endosomal escape peptides (i.e., TAT, GALA, and H6CM18) on the pharmacokinetics and distribution of an anti-carcinoembryonic antigen "catch-and-release" monoclonal antibody, 10H6, in a murine model of colorectal cancer. GALA and TAT were conjugated to 10H6 using SoluLINK technology that allowed the evaluation of peptide-to-antibody ratio by ultraviolet spectroscopy. H6CM18 was conjugated to either NHS or maleimide-modified 10H6 using an azide-modified valine-citrulline linker and copper-free click chemistry. Unmodified and peptide-conjugated 10H6 preparations were administered intravenously at 6.67 nmol/kg to mice-bearing MC38^CEA+ tumors. Unconjugated 10H6 demonstrated a clearance of 19.9 ± 1.36 mL/day/kg, with an apparent volume of distribution of 62.4 ± 7.78 mL/kg. All antibody-peptide conjugates exhibited significantly decreased plasma and tissue exposure, increased plasma clearance, and increased distribution volume. Examination of tissue-to-plasma exposure ratios showed an enhanced selectivity of 10H6-TAT for the GI tract (+25%), kidney (+24%), liver (+38%), muscle (+3%), and spleen (+33%). 10H6-GALA and 10H6-H6CM18 conjugates demonstrated decreased exposure in all tissues, relative to unmodified 10H6. All conjugates demonstrated decreased tumor exposure and selectivity; however, differences in tumor selectivity between 10H6 and 10H6-H6CM18 (maleimide) were not statistically significant. Relationships between the predicted peptide conjugate isoelectric point (pI) and pharmacokinetic parameters were bell-shaped, where pI values around 6.8-7 exhibit the slowest plasma clearance and smallest distribution volume. The data and analyses presented in this work may guide future efforts to develop immunoconjugates with cell penetrating and endosomal escape peptides.

A Physiologically Based Pharmacokinetic Framework for Quantifying Antibody Distribution Gradients from Tumors to Tumor-Draining Lymph Nodes

Immune checkpoint blockades prescribed in the neoadjuvant setting are now under active investigation for many types of tumors, and many have shown early success. The primary tumor (PT) and tumor-draining lymph node (TDLN) immune factors, along with adequate therapeutic antibody distributions to the PT and TDLN, are critical for optimal immune activation and anti-tumor efficacy in neoadjuvant immunotherapy. However, it remains largely unknown how much of the antibody can be distributed into the PT-TDLN axis at different clinical scenarios. The goal of the current work is to build a physiologically based pharmacokinetic (PBPK) model framework capable of characterizing antibody distribution gradients in the PT-TDLN axis across various clinical and pathophysiological scenarios. The model was calibrated using clinical data from immuno-PET antibody-imaging studies quantifying antibody pharmacokinetics (PK) in the blood, PTs, and TDLNs. The effects of metastatic lesion location, tumor-induced compression, and inflammation, as well as surgery, on antibody concentration gradients in the PT-TDLN axis were characterized. The PBPK model serves as a valuable tool to predict antibody exposures in various types of tumors, metastases, and the associated lymph node, supporting effective immunotherapy.

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.

A New Pharmacokinetic Model Describing the Biodistribution of Intravenously and Intratumorally Administered Superparamagnetic Iron Oxide Nanoparticles (SPIONs) in a GL261 Xenograft Glioblastoma Model

Background

Superparamagnetic iron oxide nanoparticles (SPIONs) have displayed multifunctional applications in cancer theranostics following systemic delivery. In an effort to increase the therapeutic potential of local therapies (including focal hyperthermia), nanoparticles can also be administered intratumorally. Therefore, the development of a reliable pharmacokinetic model for the prediction of nanoparticle distribution for both clinically relevant routes of delivery is of high importance.

Materials and Methods

The biodistribution of SPIONs (of two different sizes – 130 nm and 60 nm) radiolabeled with zirconium-89 or technetium-99m following intratumoral or intravenous injection was investigated in C57/Bl6 mice bearing subcutaneous GL261 glioblastomas. Based on PET/CT biodistribution data, a novel pharmacokinetic model was established for a better understanding of the pharmacokinetics of the SPIONs after both administration routes.

Results

The PET image analysis of the nanoparticles (confirmed by histology) demonstrated the presence of radiolabeled nanoparticles within the glioma site (with low amounts in the liver and spleen) at all investigated time points following intratumoral injection. The mathematical model confirmed the dynamic nanoparticle redistribution in the organism over a period of 72 h with an equilibrium reached after 100 h. Intravenous injection of nanoparticles demonstrated a different distribution pattern with a rapid particle retention in all organs (particularly in liver and spleen) and a subsequent slow release rate.

Conclusion

The mathematical model demonstrated good agreement with experimental data derived from tumor mouse models suggesting the value of this tool to predict the real-time pharmacokinetic features of SPIONs in vivo. In the future, it is planned to adapt our model to other nanoparticle formulations to more precisely describe their biodistribution in in vivo model systems.

The biodistribution of therapeutic proteins: Mechanism, implications for pharmacokinetics, and methods of evaluation

Therapeutic proteins (TPs) are a diverse drug class that include monoclonal antibodies (mAbs), recombinantly expressed enzymes, hormones and growth factors, cytokines (e.g. chemokines, interleukins, interferons), as well as a wide range of engineered fusion scaffolds containing IgG1 Fc domain for half-life extension. As the pharmaceutical industry advances more potent and selective protein-based medicines through discovery and into the clinical stages of development, it has become widely appreciated that a comprehensive understanding of the mechanisms of TP biodistribution can aid this endeavor. This review aims to highlight the literature that has advanced our understanding of the determinants of TP biodistribution. A particular emphasis is placed on the multi-faceted role of the neonatal Fc receptor (FcRn) in mAb and Fc-fusion protein disposition. In addition, characterization of the TP-target interaction at the cell-level is discussed as an essential strategy to establish pharmacokinetic-pharmacodynamic (PK/PD) relationships that may lead to more informed human dose projections during clinical development. Methods for incorporation of tissue and cell-level parameters defining these characteristics into higher-order mechanistic and semi-mechanistic PK models will also be presented.