A physiologically based pharmacokinetic model of vascular-extravascular exchanges during liver carcinogenesis: application to MRI contrast agents

Localization of drug biodistribution in a 3D-bioengineered subcutaneous neovascularized microenvironment

Local immunomodulation has shown the potential to control the immune response in a site-specific manner for wound healing, cancer, allergy, and cell transplantation, thus abrogating adverse effects associated with systemic administration of immunotherapeutics. Localized immunomodulation requires confining the biodistribution of immunotherapeutics on-site for maximal immune control and minimal systemic drug exposure. To this end, we developed a 3D-printed subcutaneous implant termed 'NICHE', consisting of a bioengineered vascularized microenvironment enabled by sustained drug delivery on-site. The NICHE was designed as a platform technology for investigating local immunomodulation in the context of cell therapeutics and cancer vaccines. Here we studied the ability of the NICHE to localize the PK and biodistribution of different model immunomodulatory agents in vivo. For this, we first performed a mechanistic evaluation of the microenvironment generated within and surrounding the NICHE, with emphasis on the parameters related to molecular transport. Second, we longitudinally studied the biodistribution of ovalbumin, cytotoxic T lymphocyte-associated antigen-4-Ig (CTLA4Ig), and IgG delivered locally via NICHE over 30 days. Third, we used our findings to develop a physiologically-based pharmacokinetic (PBPK) model. Despite dense and mature vascularization within and surrounding the NICHE, we showed sustained orders of magnitude higher molecular drug concentrations within its microenvironment as compared to systemic circulation and major organs. Further, the PBPK model was able to recapitulate the biodistribution of the 3 molecules with high accuracy (r ​> ​0.98). Overall, the NICHE and the PBPK model represent an adaptable platform for the investigation of local immunomodulation strategies for a wide range of biomedical applications.

Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach

Perfusion properties can be estimated from pharmacokinetic models applied to DCE-MRI data using curve fitting algorithms; however, these suffer from drawbacks including the local minimum problem and substantial computational time. Here, a dictionary matching approach is proposed as an alternative. Curve fitting and dictionary matching were applied to simulated data using the dual-input single-compartment model with known perfusion property values and 5 in vivo DCE-MRI datasets. In simulation at SNR 60 dB, the dictionary estimate had a mean percent error of 0.4-1.0% for arterial fraction, 0.5-1.4% for distribution volume, and 0.0% for mean transit time. The curve fitting estimate had a mean percent error of 1.1-2.1% for arterial fraction, 0.5-1.3% for distribution volume, and 0.2-1.8% for mean transit time. In vivo, dictionary matching and curve fitting showed no statistically significant differences in any of the perfusion property measurements in any of the 10 ROIs between the methods. In vivo, the dictionary method performed over 140-fold faster than curve fitting, obtaining whole volume perfusion maps in just over 10 s. This study establishes the feasibility of using a dictionary matching approach as a new and faster way of estimating perfusion properties from pharmacokinetic models in DCE-MRI.

A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery

Towards clinical translation of cancer nanomedicine, it is important to systematically investigate the various parameters related to nanoparticle (NP) physicochemical properties, tumor characteristics, and inter-individual variability that affect the tumor delivery efficiency of therapeutic nanomaterials. Comprehensive investigation of these parameters using traditional experimental approaches is impractical due to the vast parameter space; mathematical models provide a more tractable approach to navigate through such a multidimensional space. To this end, we have developed a predictive mathematical model of whole-body NP pharmacokinetics and their tumor delivery in vivo, and have conducted local and global sensitivity analyses to identify the factors that result in low tumor delivery efficiency and high off-target accumulation of NPs. Our analyses reveal that NP degradation rate, tumor blood viscosity, NP size, tumor vascular fraction, and tumor vascular porosity are the key parameters in governing NP kinetics in the tumor interstitium. The impact of these parameters on tumor delivery efficiency of NPs is discussed, and optimal values for maximizing NP delivery are presented.

Interstitial fluid pressure and associated lymph node metastasis revealed in tumors by dynamic contrast-enhanced MRI

Elevated interstitial fluid pressure (IFP) in tumors can cause metastatic dissemination and treatment resistance, but its study poses a challenge because of a paucity of noninvasive imaging strategies. In this study, we address this issue by reporting the development of a noninvasive tool to assess tumor IFP and interstitial hypertension-induced lymph node metastasis. Using mouse xenograft models of several types of human cancer, we used gadolinium diethylene-triamine penta-acetic acid (Gd-DTPA) as a contrast agent for dynamic contrast-enhanced MRI (DCE-MRI). Immediately after Gd-DTPA administration, a high-signal-intensity rim was observed in the tumor periphery, which moved outward with time. Assuming the velocity of Gd-DTPA to be equal to the fluid flow velocity, we used a simple model of peritumoral interstitial fluid flow to calculate the fluid flow velocity at the tumor surface (v(0)) based on the rim movement. Significant positive correlations were found between v(0) and IFP in all tumor xenografts. Moreover, the primary tumors of metastasis-positive mice displayed higher IFP and v(0) than the primary tumors of metastasis-negative mice. Findings were confirmed in cervical cancer patients with pelvic lymph node metastases, where we found v(0) to be higher compared with patients without lymph node involvement (P < 0.00001). Together, these findings establish that Gd-DTPA-based DCE-MRI can noninvasively visualize tumor IFP, and they reveal the potential for v(0) determined by this method to serve as a novel general biomarker of tumor aggressiveness.

BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems

We developed a detailed, whole-body physiologically based pharmacokinetic (PBPK) modeling tool for calculating the distribution of pharmaceutical agents in the various tissues and organs of a human or animal as a function of time. Ordinary differential equations (ODEs) represent the circulation of body fluids through organs and tissues at the macroscopic level, and the biological transport mechanisms and biotransformations within cells and their organelles at the molecular scale. Each major organ in the body is modeled as composed of one or more tissues. Tissues are made up of cells and fluid spaces. The model accounts for the circulation of arterial and venous blood as well as lymph. Since its development was fueled by the need to accurately predict the pharmacokinetic properties of imaging agents, BioDMET is more complex than most PBPK models. The anatomical details of the model are important for the imaging simulation endpoints. Model complexity has also been crucial for quickly adapting the tool to different problems without the need to generate a new model for every problem. When simpler models are preferred, the non-critical compartments can be dynamically collapsed to reduce unnecessary complexity. BioDMET has been used for imaging feasibility calculations in oncology, neurology, cardiology, and diabetes. For this purpose, the time concentration data generated by the model is inputted into a physics-based image simulator to establish imageability criteria. These are then used to define agent and physiology property ranges required for successful imaging. BioDMET has lately been adapted to aid the development of antimicrobial therapeutics. Given a range of built-in features and its inherent flexibility to customization, the model can be used to study a variety of pharmacokinetic and pharmacodynamic problems such as the effects of inter-individual differences and disease-states on drug pharmacokinetics and pharmacodynamics, dosing optimization, and inter-species scaling. While developing a tool to aid imaging agent and drug development, we aimed at accelerating the acceptance and broad use of PBPK modeling by providing a free mechanistic PBPK software that is user friendly, easy to adapt to a wide range of problems even by non-programmers, provided with ready-to-use parameterized models and benchmarking data collected from the peer-reviewed literature.

Multiscale model of liver DCE-MRI towards a better understanding of tumor complexity

The use of quantitative imaging for the characterization of hepatic tumors in magnetic resonance imaging (MRI) can improve the diagnosis and therefore the treatment of these life-threatening tumors. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. These processes occur at variable spatial and temporal scales. We propose a multiscale model of liver dynamic contrast-enhanced (DCE) MRI in order to better understand the tumor complexity in images. Our design couples a model of the organ (tissue and vasculature) with a model of the image acquisition. At the macroscopic scale, vascular trees take a prominent place. Regarding the formation of MRI images, we propose a distributed model of parenchymal biodistribution of extracellular contrast agents. Model parameters can be adapted to simulate the tumor development. The sensitivity of the multiscale model of liver DCE-MRI was studied through observations of the influence of two physiological parameters involved in carcinogenesis (arterial flow and capillary permeability) on its outputs (MRI images at arterial and portal phases). Finally, images were simulated for a set of parameters corresponding to the five stages of hepatocarcinogenesis (from regenerative nodules to poorly differentiated HepatoCellular Carcinoma).