Comparison of three physiologically-based pharmacokinetic models for the prediction of contrast agent distribution measured by dynamic MR imaging

The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment

Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with K^trans values ranging from 0.01 to 0.8 min⁻¹ and fixed V_e = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both K^trans and V_e. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.

An Anthropomorphic Digital Reference Object (DRO) for Simulation and Analysis of Breast DCE MRI Techniques

Advances in accelerated magnetic resonance imaging (MRI) continue to push the bounds on achievable spatial and temporal resolution while maintaining a clinically acceptable image quality. Validation tools, including numerical simulations, are needed to characterize the repeatability and reproducibility of such methods for use in quantitative imaging applications. We describe the development of a simulation framework for analyzing and optimizing accelerated MRI acquisition and reconstruction techniques used in dynamic contrast enhanced (DCE) breast imaging. The simulation framework, in the form of a digital reference object (DRO), consists of four modules that control different aspects of the simulation, including the appearance and physiological behavior of the breast tissue as well as the MRI acquisition settings, to produce simulated k-space data for a DCE breast exam. The DRO design and functionality are described along with simulation examples provided to show potential applications of the DRO. The included simulation results demonstrate the ability of the DRO to simulate a variety of effects including the creation of simulated lesions, tissue enhancement modeled by the generalized kinetic model, T1-relaxation, fat signal precession and saturation, acquisition SNR, and changes in temporal resolution.

The loss of DHX15 impairs endothelial energy metabolism, lymphatic drainage and tumor metastasis in mice

DHX15 is a downstream substrate for Akt1, which is involved in key cellular processes affecting vascular biology. Here, we explored the vascular regulatory function of DHX15. Homozygous DHX15 gene deficiency was lethal in mouse and zebrafish embryos. DHX15-/- zebrafish also showed downregulation of VEGF-C and reduced formation of lymphatic structures during development. DHX15+/- mice depicted lower vascular density and impaired lymphatic function postnatally. RNAseq and proteome analysis of DHX15 silenced endothelial cells revealed differential expression of genes involved in the metabolism of ATP biosynthesis. The validation of these results demonstrated a lower activity of the Complex I in the mitochondrial membrane of endothelial cells, resulting in lower intracellular ATP production and lower oxygen consumption. After injection of syngeneic LLC1 tumor cells, DHX15+/- mice showed partially inhibited primary tumor growth and reduced lung metastasis. Our results revealed an important role of DHX15 in vascular physiology and pave a new way to explore its potential use as a therapeutical target for metastasis treatment.

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation

PURPOSE

Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model.

METHODS

Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation.

RESULTS

Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error.

CONCLUSIONS

DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

Gadolinium-labeled affibody-XTEN recombinant vector for detection of HER2+ lesions of ovarian cancer lung metastasis using quantitative MRI

Ovarian cancer has the highest mortality rate among all gynecologic malignancies. HER2⁺ ovarian cancer is a subtype that is aggressive and associated with metastasis to distant sites such as the lungs. Therefore, accurate biological characterization of metastatic lesions is vital as it helps physicians select the most effective treatment strategy. Functional imaging of ovarian cancer with PET/CT is routinely used in the clinic to detect metastatic disease and evaluate treatment response. However, this imaging method does not provide information regarding the presence or absence of cancer-specific cell surface biomarkers such as HER2. As a result, this method does not help physicians decide whether to choose immunotherapy to treat metastasis. To differentiate the HER2⁺ from HER2¯ lesions in ovarian cancer lung metastasis, AbX₅₀C_4:Gd vector composed of a HER2 targeting affibody and XTEN peptide was genetically engineered. It was then labeled with gadolinium (Gd) via a stable linker. The vector was characterized physicochemically and biologically to determine its purity, molecular weight, hydrodynamic size and surface charge, stability in serum, endotoxin levels, relaxivity and ability to target the HER2 antigen. Then, SCID mice were implanted with SKOV-3 (HER2⁺) and OVASC-1 (HER2¯) tumors in the lungs and injected with the Gd-labeled HER2 targeted AbX₅₀C₄:Gd vector. The mice were imaged using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), followed by R1-mapping and quantitative analysis of the images. Our data demonstrate that the developed HER2-targeted vector can differentiate HER2⁺ lung metastasis from HER2¯ lesions using DCE-MRI. The developed vector could potentially be used in conjunction with other imaging modalities to prescreen patients and identify candidates for immunotherapy while triaging those who may not be considered responsive.

Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI

PURPOSE

Current breast DCE-MRI strategies provide high sensitivity for cancer detection but are known to be insufficient in fully capturing rapidly changing contrast kinetics at high spatial resolution across both breasts. Advanced acquisition and reconstruction strategies aim to improve spatial and temporal resolution and increase specificity for disease characterization. In this work, we evaluate the spatial and temporal fidelity of a modified data-driven low-rank-based model (known as MOCCO, model consistency condition) compressed-sensing (CS) reconstruction compared to CS with temporal total variation with radial acquisition for high spatial-temporal breast DCE MRI.

METHODS

Reconstruction performance was characterized using numerical simulations of a golden-angle stack-of-stars breast DCE-MRI acquisition at 5-second temporal resolution. Specifically, MOCCO was compared with CS total variation and conventional SENSE reconstructions. The temporal model for MOCCO was prelearned over the source data, whereas CS total variation was performed using a first-order temporal gradient sparsity transform.

RESULTS

The MOCCO reconstruction was able to capture rapid lesion kinetics while providing high image quality across a range of optimal regularization values. It also recovered kinetics in small lesions (1.5 mm) in line-profile analysis and error images, whereas g-factor maps showed relatively low and constant values with no significant artifacts. The CS-TV method demonstrated either recovery of high spatial resolution with reduced temporal accuracy using large regularization values, or recovery of rapid lesion kinetics with reduced image quality using low regularization values.

CONCLUSION

Simulations demonstrated that MOCCO with radial acquisition provides a robust imaging technique for improving temporal fidelity, while maintaining high spatial resolution and image quality in the setting of bilateral breast DCE MRI.

A theoretical framework for determining cerebral vascular function and heterogeneity from dynamic susceptibility contrast MRI

Mapping the complex heterogeneity of vascular tissue in the brain is important for understanding cerebrovascular disease. In this translational study, we build on previous work using vessel architectural imaging (VAI) and present a theoretical framework for determining cerebral vascular function and heterogeneity from dynamic susceptibility contrast magnetic resonance imaging (MRI). Our tissue model covers realistic structural architectures for vessel branching and orientations, as well as a range of hemodynamic scenarios for blood flow, capillary transit times and oxygenation. In a typical image voxel, our findings show that the apparent MRI relaxation rates are independent of the mean vessel orientation and that the vortex area, a VAI-based parameter, is determined by the relative oxygen saturation level and the vessel branching of the tissue. Finally, in both simulated and patient data, we show that the relative distributions of the vortex area parameter as a function of capillary transit times show unique characteristics in normal-appearing white and gray matter tissue, whereas tumour-voxels in comparison display a heterogeneous distribution. Collectively, our study presents a comprehensive framework that may serve as a roadmap for in vivo and per-voxel determination of vascular status and heterogeneity in cerebral tissue.

Assessment of DCE-MRI parameters for brain tumors through implementation of physiologically-based pharmacokinetic model approaches for Gd-DOTA

Dynamic-contrast enhanced magnetic resonance imaging (DCE-MRI) is used for detailed characterization of pathology of lesions sites, such as brain tumors, by quantitative analysis of tracer's data through the use of pharmacokinetic (PK) models. A key component for PK models in DCE-MRI is the estimation of the concentration-time profile of the tracer in a nearby vessel, referred as Arterial Input Function (AIF). The aim of this work was to assess through full body physiologically-based pharmacokinetic (PBPK) model approaches the PK profile of gadoteric acid (Gd-DOTA) and explore potential application for parameter estimation in DCE-MRI based on PBPK-derived AIFs. The PBPK simulations were generated through Simcyp(®) platform and the predicted PK parameters for Gd-DOTA were compared with available clinical data regarding healthy volunteers and renal impairment patients. The assessment of DCE-MRI parameters was implemented by utilizing similar virtual profiles based on gender, age and weight to clinical profiles of patients diagnosed with glioblastoma multiforme. The PBPK-derived AIFs were then used to compute DCE-MRI parameters through the Extended Tofts Model and compared with the corresponding ones derived from image-based AIF computation. The comparison involved: (i) image measured AIF of patients vs AIF of in silico profile, and, (ii) population average AIF vs in silico mean AIFs. The results indicate that PBPK-derived AIFs allowed the estimation of comparable imaging biomarkers with those calculated from typical DCE-MRI image analysis. The incorporation of PBPK models and potential utilization of in silico profiles to real patient data, can provide new perspectives in DCE-MRI parameter estimation and data analysis.

Identifying the association between contrast enhancement pattern, surgical resection, and prognosis in anaplastic glioma patients

INTRODUCTION

Contrast enhancement observable on magnetic resonance (MR) images reflects the destructive features of malignant gliomas. This study aimed to investigate the relationship between radiologic patterns of tumor enhancement, extent of resection, and prognosis in patients with anaplastic gliomas (AGs).

METHODS

Clinical data from 268 patients with histologically confirmed AGs were retrospectively analyzed. Contrast enhancement patterns were classified based on preoperative T1-contrast MR images. Univariate and multivariate analyses were performed to evaluate the prognostic value of MR enhancement patterns on progression-free survival (PFS) and overall survival (OS).

RESULTS

The pattern of tumor contrast enhancement was associated with the extent of surgical resection in AGs. A gross total resection was more likely to be achieved for AGs with focal enhancement than those with diffuse (p = 0.001) or ring-like (p = 0.024) enhancement. Additionally, patients with focal-enhanced AGs had a significantly longer PFS and OS than those with diffuse (log-rank, p = 0.025 and p = 0.031, respectively) or ring-like (log-rank, p = 0.008 and p = 0.011, respectively) enhanced AGs. Furthermore, multivariate analysis identified the pattern of tumor enhancement as a significant predictor of PFS (p = 0.016, hazard ratio [HR] = 1.485) and OS (p = 0.030, HR = 1.446).

CONCLUSION

Our results suggested that the contrast enhancement pattern on preoperative MR images was associated with the extent of resection and predictive of survival outcomes in AG patients.

Dynamic contrast-enhanced MRI: Study of inter-software accuracy and reproducibility using simulated and clinical data

PURPOSE

To test the reproducibility and accuracy of pharmacokinetic parameter measurements on five analysis software packages (SPs) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), using simulated and clinical data.

MATERIALS AND METHODS

This retrospective study was Institutional Review Board-approved. Simulated tissues consisted of pixel clusters of calculated dynamic signal changes for combinations of Tofts model pharmacokinetic parameters (volume transfer constant [K(trans) ], extravascular extracellular volume fraction [ve ]), longitudinal relaxation time (T1 ). The clinical group comprised 27 patients treated for rectal cancer, with 36 3T DCE-MR scans performed between November 2012 and February 2014, including dual-flip-angle T1 mapping and a dynamic postcontrast T1 -weighted, 3D spoiled gradient-echo sequence. The clinical and simulated images were postprocessed with five SPs to measure K(trans) , ve , and the initial area under the gadolinium curve (iAUGC). Modified Bland-Altman analysis was conducted, intraclass correlation coefficients (ICCs) and within-subject coefficients of variation were calculated.

RESULTS

Thirty-one examinations from 23 patients were of sufficient technical quality and postprocessed. Measurement errors were observed on the simulated data for all the pharmacokinetic parameters and SPs, with a bias ranging from -0.19 min(-1) to 0.09 min(-1) for K(trans) , -0.15 to 0.01 for ve , and -0.65 to 1.66 mmol.L(-1) .min for iAUGC. The ICC between SPs revealed moderate agreement for the simulated data (K(trans) : 0.50; ve : 0.67; iAUGC: 0.77) and very poor agreement for the clinical data (K(trans) : 0.10; ve : 0.16; iAUGC: 0.21).

CONCLUSION

Significant errors were found in the calculated DCE-MRI pharmacokinetic parameters for the perfusion analysis SPs, resulting in poor inter-software reproducibility. J. Magn. Reson. Imaging 2016;43:1288-1300.

Advanced magnetic resonance imaging of the physical processes in human glioblastoma

The most common malignant primary brain tumor, glioblastoma multiforme (GBM) is a devastating disease with a grim prognosis. Patient survival is typically less than two years and fewer than 10% of patients survive more than five years. Magnetic resonance imaging (MRI) can have great utility in the diagnosis, grading, and management of patients with GBM as many of the physical manifestations of the pathologic processes in GBM can be visualized and quantified using MRI. Newer MRI techniques such as dynamic contrast enhanced and dynamic susceptibility contrast MRI provide functional information about the tumor hemodynamic status. Diffusion MRI can shed light on tumor cellularity and the disruption of white matter tracts in the proximity of tumors. MR spectroscopy can be used to study new tumor tissue markers such as IDH mutations. MRI is helping to noninvasively explore the link between the molecular basis of gliomas and the imaging characteristics of their physical processes. We, here, review several approaches to MR-based imaging and discuss the potential for these techniques to quantify the physical processes in glioblastoma, including tumor cellularity and vascularity, metabolite expression, and patterns of tumor growth and recurrence. We conclude with challenges and opportunities for further research in applying physical principles to better understand the biologic process in this deadly disease. See all articles in this Cancer Research section, "Physics in Cancer Research."

Advanced magnetic resonance imaging methods for planning and monitoring radiation therapy in patients with high-grade glioma

This review explores how the integration of advanced imaging methods with high-quality anatomical images significantly improves the characterization, target definition, assessment of response to therapy, and overall management of patients with high-grade glioma. Metrics derived from diffusion-, perfusion-, and susceptibility-weighted magnetic resonance imaging in conjunction with magnetic resonance spectroscopic imaging, allows us to characterize regions of edema, hypoxia, increased cellularity, and necrosis within heterogeneous tumor and surrounding brain tissue. Quantification of such measures may provide a more reliable initial representation of tumor delineation and response to therapy than changes in the contrast-enhancing or T2 lesion alone and have a significant effect on targeting resection, planning radiation, and assessing treatment effectiveness. In the long term, implementation of these imaging methodologies can also aid in the identification of recurrent tumor and its differentiation from treatment-related confounds and facilitate the detection of radiationinduced vascular injury in otherwise normal-appearing brain tissue.

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data

Background

DCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity.

Results

Pixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to several pharmacokinetic models using the Levenberg-Marquardt algorithm (LMA). DCE@urLAB also includes the semi-quantitative parametric and heuristic analysis approaches commonly used in practice. This software application has been programmed in the Interactive Data Language (IDL) and tested both with publicly available simulated data and preclinical studies from tumor-bearing mouse brains.

Conclusions

A user-friendly solution for applying pharmacokinetic and non-quantitative analysis DCE-MRI in preclinical studies has been implemented and tested. The proposed tool has been specially designed for easy selection of multi-pixel ROIs. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/.

T2*-correction in dynamic contrast-enhanced MRI from double-echo acquisitions

PURPOSE

To evaluate the importance of T2*-effects on the arterial input function (AIF) and on the resulting dynamic parameter estimation in dynamic contrast-enhanced (DCE) MRI of high-grade gliomas.

MATERIALS AND METHODS

Seven patients with high-grade gliomas were imaged in total 50 times using a double-echo DCE sequence. Kinetic analysis using the extended Tofts model was performed using AIFs with and without correction for T2*-effects, and the resulting estimates of the transfer constant (K(trans) ), blood plasma volume (vp ), and the rate constant (kep ) were compared. Numerical simulations were done for comparison with clinical results as well as to further investigate the dependency of parameter values on the magnitude of T2*-induced errors.

RESULTS

All kinetic parameters were found to be overestimated if T2*-effects in the AIF were not accounted for; with vp being most severely affected. The relative error in each parameter was dependent on the absolute parameter magnitude, resulting in incorrect parametric tumor distributions in the presence of uncorrected AIF T2*-effects.

CONCLUSION

In DCE, a sufficiently short echo time should be used or corrections for T2*-effects based on double-echo acquisition should be made for correct quantification of kinetic parameters.

Vessel architectural imaging identifies cancer patient responders to anti-angiogenic therapy

Measurement of vessel caliber by Magnetic Resonance Imaging (MRI) is a valuable technique for in vivo monitoring of hemodynamic status and vascular development, especially in the brain. Here, we introduce a new paradigm in MRI coined as Vessel Architectural Imaging (VAI) that exploits an intriguing and overlooked temporal shift in the MR signal forming the basis for vessel caliber estimation and show how this phenomenon can reveal new information on vessel type and function not assessed by any other non-invasive imaging technique. We also show how this biomarker can provide novel biological insights into the treatment of cancer patients. As an example, we demonstrate using VAI that anti-angiogenic therapy can improve microcirculation and oxygen saturation levels and reduce vessel calibers in patients with recurrent glioblastomas, and more crucially, that patients with these responses have prolonged survival. Thus, VAI has the potential to identify patients who would benefit from therapies.

Magnetic resonance imaging biomarkers in hepatocellular carcinoma: association with response and circulating biomarkers after sunitinib therapy

Background

To investigate the hypothesis that MRI derived diffusion-weighted imaging (DWI) and perfusion (MRP) parameters are sensitive image biomarkers for monitoring early antiangiogenic effects and predicting progression free survival (PFS) in advanced hepatocellular carcinoma (HCC).

Methods

In this phase II clinical trial, 23 of 34 patients were included in the imaging and circulating biomarker study. DWI and MRP were performed at the baseline and at 2-weeks after initiation of sunitinib. The imaging protocol included an axial DWI sequence using b values of 50, 400 and 800 sec/mm², and MRP using a series of coronal 3D-VIBE following 20 ml of Gd-DTPA at 2 ml/sec. These parameters were compared with clinical outcome and PFS at 6-months. Correlation between changes in MRI parameters and plasma biomarkers was also evaluated.

Results

After 2-week of sunitinib, substantial Ktrans changes in HCC were observed from median baseline value 2.15 min⁻¹ to 0.94 min⁻¹ (P = 0.0001) with increases in median apparent diffusion coefficient (ADC) from 0.88 × 10^-3 mm²/s to 0.98 × 10^-3 mm²/s (P = 0.0001). Tumor size remained unchanged by RECIST and mRECIST (both P > 0.05). Patients who showed larger drop in Ktrans and Kep at 2 weeks correlated with favorable clinical outcome, and higher baseline Ktrans and larger drop in EVF correlated with longer PFS (all P < 0.05). There was a significant association between a decrease in sVEGFR2 and the drop in Ktrans and Kep (P = 0.044, P = 0.030), and a significant and borderline association between decrease in TNF-α and the drop in Ktrans and Kep, respectively (P = 0.051, P = 0.035).

Conclusion

In HCC, MRP may be a more sensitive biomarker in predicting early response and PFS following sunitinib than RECIST and mRECIST.

Trial registration

ClinicalTrials.gov: NCT00361309

Delayed contrast extravasation MRI for depicting tumor and non-tumoral tissues in primary and metastatic brain tumors

The current standard of care for newly diagnosed glioblastoma multiforme (GBM) is resection followed by radiotherapy with concomitant and adjuvant temozolomide. Recent studies suggest that nearly half of the patients with early radiological deterioration post treatment do not suffer from tumor recurrence but from pseudoprogression. Similarly, a significant number of patients with brain metastases suffer from radiation necrosis following radiation treatments. Conventional MRI is currently unable to differentiate tumor progression from treatment-induced effects. The ability to clearly differentiate tumor from non-tumoral tissues is crucial for appropriate patient management. Ten patients with primary brain tumors and 10 patients with brain metastases were scanned by delayed contrast extravasation MRI prior to surgery. Enhancement subtraction maps calculated from high resolution MR images acquired up to 75 min after contrast administration were used for obtaining stereotactic biopsies. Histological assessment was then compared with the pre-surgical calculated maps. In addition, the application of our maps for prediction of progression was studied in a small cohort of 13 newly diagnosed GBM patients undergoing standard chemoradiation and followed up to 19.7 months post therapy. The maps showed two primary enhancement populations: the slow population where contrast clearance from the tissue was slower than contrast accumulation and the fast population where clearance was faster than accumulation. Comparison with histology confirmed the fast population to consist of morphologically active tumor and the slow population to consist of non-tumoral tissues. Our maps demonstrated significant correlation with perfusion-weighted MR data acquired simultaneously, although contradicting examples were shown. Preliminary results suggest that early changes in the fast volumes may serve as a predictor for time to progression. These preliminary results suggest that our high resolution MRI-based delayed enhancement subtraction maps may be applied for clear depiction of tumor and non-tumoral tissues in patients with primary brain tumors and patients with brain metastases.

Sampling requirements in DCE-MRI based analysis of high grade gliomas: simulations and clinical results

PURPOSE

To investigate the effect of variations in temporal resolution and total measurement times on the estimations of kinetic parameters derived from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas (HGGs).

MATERIALS AND METHODS

DCE-MRI with high temporal resolution (dynamic sampling time (T(s)) = 2.1 s and 3.4 s) and total sampling time (T(acq)) of 5.2 min was acquired in 101 examinations from 15 patients. Using the modified Tofts model K(trans), k(ep) v(e) and v(p) were estimated. The effects of increasing T(s) and reducing T(acq) on the estimated kinetic parameters were estimated through down-sampling and data truncation, and the results were compared with numerical simulations.

RESULTS

There was an overall dependence of all four kinetic parameters on T(s) and T(acq). Increasing T(s) resulted in under-estimation of K(trans) and over-estimation of V(p), whereas k(ep) and V(e) varied in a less predictable manner. Reducing T(acq) resulted in over-estimation of K(trans) and k(ep) and under-estimation of v(p) and v(e). Increasing T(s) and reducing T(acq) resulted in increased relative error for all four parameters.

CONCLUSION

Estimated K(trans), K(ep), and V(e) in HGGs were within 15% of the high sampling rate reference values for T(s) <20 s. Increasing T(s) and reducing T(acq) leads to reduced precision of the estimated values.

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.

Assessment of therapeutic response and treatment planning for brain tumors using metabolic and physiological MRI

MRI is routinely used for diagnosis, treatment planning and assessment of response to therapy for patients with glioma. Gliomas are spatially heterogeneous and infiltrative lesions that are quite variable in terms of their response to therapy. Patients classified as having low-grade histology have a median overall survival of 7 years or more, but need to be monitored carefully to make sure that their tumor does not upgrade to a more malignant phenotype. Patients with the most aggressive grade IV histology have a median overall survival of 12-15 months and often undergo multiple surgeries and adjuvant therapies in an attempt to control their disease. Despite improvements in the spatial resolution and sensitivity of anatomic images, there remain considerable ambiguities in the interpretation of changes in the size of the gadolinium-enhancing lesion on T(1) -weighted images as a measure of treatment response, and in differentiating between treatment effects and infiltrating tumor within the larger T(2) lesion. The planning of focal therapies, such as surgery, radiation and targeted drug delivery, as well as a more reliable assessment of the response to therapy, would benefit considerably from the integration of metabolic and physiological imaging techniques into routine clinical MR examinations. Advanced methods that have been shown to provide valuable data for patients with glioma are diffusion, perfusion and spectroscopic imaging. Multiparametric examinations that include the acquisition of such data are able to assess tumor cellularity, hypoxia, disruption of normal tissue architecture, changes in vascular density and vessel permeability, in addition to the standard measures of changes in the volume of enhancing and nonenhancing anatomic lesions. This is particularly critical for the interpretation of the results of Phase I and Phase II clinical trials of novel therapies, which are increasingly including agents that are designed to have anti-angiogenic and anti-proliferative properties as opposed to having a direct effect on tumor cell viability.

Changes in vascular permeability and expression of different angiogenic factors following anti-angiogenic treatment in rat glioma

BACKGROUND

Anti-angiogenic treatments of malignant tumors targeting vascular endothelial growth factor receptors (VEGFR) tyrosine kinase are being used in different early stages of clinical trials. Very recently, VEGFR tyrosine kinase inhibitor (Vetanalib, PTK787) was used in glioma patient in conjunction with chemotherapy and radiotherapy. However, changes in the tumor size, tumor vascular permeability, vascular density, expression of VEGFR2 and other angiogenic factors in response to PTK787 are not well documented. This study was to determine the changes in tumor size, vascular permeability, fractional plasma volume and expression of VEGFR2 in PTK787 treated U-251 glioma rat model by in vivo magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). The findings were validated with histochemical and western blot studies.

METHODOLOGIES AND PRINCIPAL FINDINGS

Seven days after implantation of U251 glioma cells, animals were treated with either PTK787 or vehicle-only for two weeks, and then tumor size, tumor vascular permeability transfer constant (K(trans)), fractional plasma volume (fPV) and expression of VEGFR2 and other relevant angiogenic factors were assessed by in vivo MRI and SPECT (Tc-99-HYNIC-VEGF), and by immunohistochemistry and western blot analysis. Dynamic contrast-enhanced MRI (DCE-MRI) using a high molecular weight contrast agent albumin-(GdDTPA) showed significantly increased K(trans) at the rim of the treated tumors compared to that of the central part of the treated as well as the untreated (vehicle treated) tumors. Size of the tumors was also increased in the treated group. Expression of VEGFR2 detected by Tc-99m-HYNIC-VEGF SPECT also showed significantly increased activity in the treated tumors. In PTK787-treated tumors, histological staining revealed increase in microvessel density in the close proximity to the tumor border. Western blot analysis indicated increased expression of VEGF, SDF-1, HIF-1alpha, VEGFR2, VEGFR3 and EGFR at the peripheral part of the treated tumors compared to that of central part of the treated tumors. Similar expression patters were not observed in vehicle treated tumors.

CONCLUSION

These findings indicate that PTK787 treatment induced over expression of VEGF as well as the Flk-1/VEGFR2 receptor tyrosine kinase, especially at the rim of the tumor, as proven by DCE-MRI, SPECT imaging, immunohistochemistry and western blot.

PKQuest_Java: free, interactive physiologically based pharmacokinetic software package and tutorial

Background

Physiologically based pharmacokinetics (PBPK) uses a realistic organ model to describe drug kinetics. The blood-tissue exchange of each organ is characterized by its volume, perfusion, metabolism, capillary permeability and blood/tissue partition coefficient. PBPK applications require both sophisticated mathematical modeling software and a reliable complete set of physiological parameters. Currently there are no software packages available that combine ease of use with the versatility that is required of a general PBPK program.

Findings

The program is written in Java and is available for free download at . Included in the download is a detailed tutorial that discusses the pharmacokinetics of 6 solutes (D₂O, amoxicillin, desflurane, propofol, ethanol and thiopental) illustrated using experimental human pharmacokinetic data. The complete PBPK description for each solute is stored in Excel spreadsheets that are included in the download. The main features of the program are: 1) Intuitive and versatile interactive interface; 2) Absolute and semi-logarithmic graphical output; 3) Pre-programmed optimized human parameter data set (but, arbitrary values can be input); 4) Time dependent changes in the PBPK parameters; 5) Non-linear parameter optimization; 6) Unique approach to determine the oral "first pass metabolism" of non-linear solutes (e.g. ethanol); 7) Pulmonary perfusion/ventilation heterogeneity for volatile solutes; 8) Input and output of Excel spreadsheet data; 9) Antecubital vein sampling.

Conclusion

PKQuest_Java is a free, easy to use, interactive PBPK software routine. The user can either directly use the pre-programmed optimized human or rat data set, or enter an arbitrary data set. It is designed so that drugs that are classified as "extracellular" or "highly fat soluble" do not require information about tissue/blood partition coefficients and can be modeled by a minimum of user input parameters. PKQuest_Java, along with the included tutorial, could be used as the basis of an interactive, on-line, pharmacokinetic course.