Non-invasive imaging of interstitial fluid transport parameters in solid tumors in vivo

The physiological limits of bispecific monoclonal antibody tissue targeting specificity

Bispecific monoclonal antibodies (bsmAbs) are expected to provide targeted drug delivery that overcomes the dose-limiting toxicities often accompanying antibody-drug conjugates (ADC) in clinical practice. Much attention has been paid in the past to target selection, mAb affinities and the payload linker design, but challenges remain. Here, we demonstrate, by physiologically based pharmacokinetic (PBPK) in silico modeling and simulation, that the tissue-targeting accuracy of mono- and bispecific antibody therapeutics is substantially limited by normal physiological characteristics like organ volumes, blood flow rates, lymphatic circulation, and rates of extravasation. Only a small fraction of blood flows through solid tumor, where the diffusion-driven extravasation is relatively slow compared with many other organs. EGFR and HER2 are used as model antigens based on their experimentally measured tissue and tumor expression levels, but the approach is generic and can account for the cellular expression variation of targets. The model confirms experimental observations that only about 0.1-1% of the dosed mAb is likely to reach the tumor, while the rest ends up in healthy tissues due to target-mediated internalization and nonspecific uptake. The model suggests that the dual-positive tumor cell targeting specificity with bispecific antibodies is likely to be higher at lower drug concentrations and doses. However, this can be offset by elevated drug exposure in more accessible healthy tissues, primarily endothelium. The balance of exposure can be shifted toward tumor cells by using higher doses, albeit at the expense of more extensive target engagement elsewhere in the body, suggesting the need to adapt the toxicity of the payload if ADCs are considered. We suggest that PBPK modeling can guide and support biologics and bsmAb development, from target evaluation and drug optimization to therapeutic dose selection.

Multiphysics simulation of liposome release from hydrogels for cavity filling following patient-specific breast tumor surgery

Several studies have recommended the use of hydrogels for localized targeted delivery of chemotherapeutic drugs following tumor removal surgery. This approach aims to both fill the cavity and prevent cancer recurrence. The use of Multiphysics-based simulation emerges as a valuable strategy for minimizing experimental work, providing detailed insights into how drug release occurs in the tissue, and enabling the optimization of the design. In this study, we introduced a mathematical model, utilizing experimental data, to investigate the transport of liposomes carrying MZ1 from a thermosensitive hydrogel and their impact on the viability of breast cancer cells. The proposed comprehensive model considers not just the transport within the interstitial tissue, represented as a porous medium, but also the uptake by cells and its influence on cell viability, along with the potential lymphatic drainage. The six real patient-specific tumor shapes extracted from MRI scans were used to investigate how the size and form of the tumor can modify the transport pattern. The computational results revealed that the concentration of liposomes in the tissue is significantly influenced by their release from the hydrogel, which proved to be the limiting step. Liposome concentrations of approximately 0.1 % weight were found to be sufficient in ensuring minimal cell survival in the vicinity of the tumor.

Mathematical Models for Ultrasound Elastography: Recent Advances to Improve Accuracy and Clinical Utility

Changes in biomechanical properties such as elasticity modulus, viscosity, and poroelastic features are linked to the health status of biological tissues. Ultrasound elastography is a non-invasive imaging tool that quantitatively maps these biomechanical characteristics for diagnostic and treatment monitoring purposes. Mathematical models are essential in ultrasound elastography as they convert the raw data obtained from tissue displacement caused by ultrasound waves into the images observed by clinicians. This article reviews the available mathematical frameworks of continuum mechanics for extracting the biomechanical characteristics of biological tissues in ultrasound elastography. Continuum-mechanics-based approaches such as classical viscoelasticity, elasticity, and poroelasticity models, as well as nonlocal continuum-based models, are described. The accuracy of ultrasound elastography can be increased with the recent advancements in continuum modelling techniques including hyperelasticity, biphasic theory, nonlocal viscoelasticity, inversion-based elasticity, and incorporating scale effects. However, the time taken to convert the data into clinical images increases with more complex models, and this is a major challenge for expanding the clinical utility of ultrasound elastography. As we strive to provide the most accurate imaging for patients, further research is needed to refine mathematical models for incorporation into the clinical workflow.

Non-Invasive Imaging of Mechanical Properties of Cancers In Vivo Based on Transformations of the Eshelby's Tensor Using Compression Elastography

Knowledge of the mechanical properties is of great clinical significance for diagnosis, prognosis and treatment of cancers. Recently, a new method based on Eshelby's theory to simultaneously assess Young's modulus (YM) and Poisson's ratio (PR) in tissues has been proposed. A significant limitation of this method is that accuracy of the reconstructed YM and PR is affected by the orientation/alignment of the tumor with the applied stress. In this paper, we propose a new method to reconstruct YM and PR in cancers that is invariant to the 3D orientation of the tumor with respect to the axis of applied stress. The novelty of the proposed method resides on the use of a tensor transformation to improve the robustness of Eshelby's theory and reconstruct YM and PR of tumors with high accuracy and in realistic experimental conditions. The method is validated using finite element simulations and controlled experiments using phantoms with known mechanical properties. The in vivo feasibility of the developed method is demonstrated in an orthotopic mouse model of breast cancer. Our results show that the proposed technique can estimate the YM and PR with overall accuracy of (97.06 ± 2.42) % under all tested tumor orientations. Animal experimental data demonstrate the potential of the proposed methodology in vivo. The proposed method can significantly expand the range of applicability of the Eshelby's theory to tumors and provide new means to accurately image and quantify mechanical parameters of cancers in clinical conditions.

Modelling and breaking down the biophysical barriers to drug delivery in pancreatic cancer

The pancreatic ductal adenocarcinoma (PDAC) stroma and its inherent biophysical barriers to drug delivery are central to therapeutic resistance. This makes PDAC the most prevalent pancreatic cancer with poor prognosis. The chemotherapeutic drug gemcitabine is used against various solid tumours, including pancreatic cancer, but with only a modest effect on patient survival. The growing PDAC tumour mass with high densities of cells and extracellular matrix (ECM) proteins, i.e., collagen, results in high interstitial pressure, leading to vasculature collapse and a dense, hypoxic, mechanically stiff stroma with reduced interstitial flow, critical to drug delivery to cells. Despite this, most drug studies are performed on cellular models that neglect these biophysical barriers to drug delivery. Microfluidic technology offers a promising platform to emulate tumour biophysical characteristics with appropriate flow conditions and transport dynamics. We present a microfluidic PDAC culture model, encompassing the disease's biophysical barriers to therapeutics, to evaluate the use of the angiotensin II receptor blocker losartan, which has been found to have matrix-depleting properties, on improving gemcitabine efficacy. PDAC cells were seeded into our 5-channel microfluidic device for a 21-day culture to mimic the rigid, collagenous PDAC stroma with reduced interstitial flow, which is critical to drug delivery to the cancer cells, and for assessment with gemcitabine and losartan treatment. With losartan, our culture matrix was more porous with less collagen, resulting in increased hydraulic conductivity of the culture interstitial space and improved gemcitabine effect. We demonstrate the importance of modelling tumour biophysical barriers to successfully assess new drugs and delivery methods.

Application of quantitative protein mass spectrometric data in the early predictive analysis of membrane-bound target engagement by monoclonal antibodies

Model-informed drug discovery advocates the use of mathematical modeling and simulation for improved efficacy in drug discovery. In the case of monoclonal antibodies (mAbs) against cell membrane antigens, this requires quantitative insight into the target tissue concentration levels. Protein mass spectrometry data are often available but the values are expressed in relative, rather than in molar concentration units that are easier to incorporate into pharmacokinetic models. Here, we present an empirical correlation that converts the parts per million (ppm) concentrations in the PaxDb database to their molar equivalents that are more suitable for pharmacokinetic modeling. We evaluate the insight afforded to target tissue distribution by analyzing the likely tumor-targeting accuracy of mAbs recognizing either epidermal growth factor receptor or its homolog HER2. Surprisingly, the predicted tissue concentrations of both these targets exceed the Kd values of their respective therapeutic mAbs. Physiologically based pharmacokinetic (PBPK) modeling indicates that in these conditions only about 0.05% of the dosed mAb is likely to reach the solid tumor target cells. The rest of the dose is eliminated in healthy tissues via both nonspecific and target-mediated processes. The presented approach allows evaluation of the interplay between the target expression level in different tissues that determines the overall pharmacokinetic properties of the drug and the fraction that reaches the cells of interest. This methodology can help to evaluate the efficacy and safety properties of novel drugs, especially if the off-target cell degradation has cytotoxic outcomes, as in the case of antibody-drug conjugates.