Edward Ashton

Dynamic Contrast-Enhanced Magnetic Resonance Imaging As a Pharmacodynamic Measure of Response After Acute Dosing of AG-013736, an Oral Angiogenesis Inhibitor, in Patients With Advanced Solid Tumors: Results From a Phase I Study

PURPOSE Identifying suitable markers of biologic activity is important when assessing novel compounds such as angiogenesis inhibitors to optimize the dose and schedule of therapy. Here we present the pharmacodynamic response to acute dosing of AG-013736 measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). PATIENTS AND METHODS Thirty-six patients with advanced solid tumors were treated with various doses of AG-013736. In addition to standard measures of objective disease response and pharmacokinetic analysis, DCE-MRI scans were acquired at baseline and repeated at cycle 1--day 2 after the scheduled morning dose of the AG-013736 in 26 patients. Indicators of a vascular response, such as the volume transfer constant (K(trans)) and initial area under the curve (IAUC), were calculated to assess the effect of treatment on tumor vascular function. RESULTS Evaluable vascular response data were obtained in 17 (65%) of 26 patients. A linear correlation was found in which the percentage change from baseline to day 2 in K(trans) and IAUC was inversely proportional to AG-013736 exposure. Using a conservative a priori assumption that a > or = 50% decrease in K(trans) was indicative of an objective vascular response, a 50% decrease in K(trans) was achieved and corresponded to a plasma AUC(0-24) of > 200 ng . h/mL. CONCLUSION A sufficient decrease in tumor vascular parameters was observed at a dose chosen for additional phase II testing by conventional toxicity criteria. In addition, the day 2 vascular response measured using DCE-MRI seems to be a useful indicator of drug pharmacology, and additional research is needed to determine if it is a suitable marker for predicting clinical activity.

Detection of subpixel anomalies in multispectral infrared imagery using an adaptive Bayesian classifier

The detection of subpixel targets with unknown spectral signatures and cluttered backgrounds in multispectral imagery is a topic of great interest for remote surveillance applications. Because no knowledge of the target is assumed, the only way to accomplish such a detection is through a search for anomalous pixels. Two approaches to this problem are examined in this paper. The first is to separate the image into a number of statistical clusters by using an extension of the well-known k-means algorithm. Each bin of resultant residual vectors is then decorrelated, and the results are thresholded to provide detection. The second approach requires the formation of a probabilistic background model by using an adaptive Bayesian classification algorithm. This allows the calculation of a probability for each pixel, with respect to the model. These probabilities are then thresholded to provide detection. Both algorithms are shown to provide significant improvement over current filtering techniques for anomaly detection in experiments using multispectral IR imagery with both simulated and actual subpixel targets.

Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI

To evaluate the accuracy, reproducibility, and speed of two semiautomated methods for quantifying total white matter lesion burden in multiple sclerosis (MS) patients with respect to manual tracing and to other methods presented in recent literature.

Scan‐rescan variability in perfusion assessment of tumors in MRI using both model and data‐derived arterial input functions

To evaluate the contribution to scan‐rescan coefficient of variation (CV) of patient‐specific arterial input function (AIF) measurement in dynamic contrast‐enhanced MRI (DCE‐MRI) data, and to determine whether any advantage or disadvantage to using a data‐derived arterial input function is related to the anatomical location of the target lesion.

Phase 1 First-in-Human Trial of the Vascular Disrupting Agent Plinabulin (NPI-2358) in Patients with Solid Tumors or Lymphomas

Purpose: Plinabulin (NPI-2358) is a vascular disrupting agent that elicits tumor vascular endothelial architectural destabilization leading to selective collapse of established tumor vasculature. Preclinical data indicated plinabulin has favorable safety and antitumor activity profiles, leading to initiation of this clinical trial to determine the recommended phase 2 dose (RP2D) and assess the safety, pharmacokinetics, and biologic activity of plinabulin in patients with advanced malignancies. Experimental Design: Patients received a weekly infusion of plinabulin for 3 of every 4 weeks. A dynamic accelerated dose titration method was used to escalate the dose from 2 mg/m2 to the RP2D, followed by enrollment of an RP2D cohort. Safety, pharmacokinetic, and cardiovascular assessments were conducted, and Dynamic contrast-enhanced MRI (DCE-MRI) scans were performed to estimate changes in tumor blood flow. Results: Thirty-eight patients were enrolled. A dose of 30 mg/m2 was selected as the RP2D based on the adverse events of nausea, vomiting, fatigue, fever, tumor pain, and transient blood pressure elevations, with DCE-MRI indicating decreases in tumor blood flow (Ktrans) from 13.5 mg/m2 (defining a biologically effective dose) with a 16% to 82% decrease in patients evaluated at 30 mg/m2. Half-life was 6.06 ± 3.03 hours, clearance was 30.50 ± 22.88 L/h, and distributive volume was 211 ± 67.9 L. Conclusions: At the RP2D of 30 mg/m2, plinabulin showed a favorable safety profile, while eliciting biological effects as evidenced by decreases in tumor blood flow, tumor pain, and other mechanistically relevant adverse events. On the basis of these results additional clinical trials were initiated with plinabulin in combination with standard chemotherapy agents. Clin Cancer Res; 16(23); 5892–99. ©2010 AACR.

Reproducibility of perfusion parameters in dynamic contrast-enhanced MRI of lung and liver tumors: Effect on estimates of patient sample size in clinical trials and on individual patient responses

OBJECTIVE Dynamic contrast-enhanced MRI (DCE-MRI) is a potentially useful noninvasive technique for assessing tissue perfusion, particularly in the context of solid tumors and targeted antiangiogenic and antivascular therapies. Our aim was to determine the reproducibility of perfusion parameters derived at DCE-MRI of tumors of the lung and liver, the most common sites of metastasis. SUBJECTS AND METHODS Patients with lung and liver tumors underwent two sequential DCE-MRI examinations 2-7 days apart without any intervening therapy. The volume transfer constant between blood plasma and the extravascular extracellular space (K(trans)) and blood-normalized initial area under the signal intensity-time curve (initial AUC(BN)) were computed with a two-compartment pharmacokinetic model. Differences in parameters were assessed with within-patient coefficients of variation. RESULTS Twenty-three patients had evaluable tumors (12 lung, 11 liver). The within-patient coefficients of variation for K(trans) and initial AUC(BN) for liver lesions were 8.9% and 9.9% and for lung lesions were 17.9% and 18.2%. Sample sizes for reductions in these parameters from 10% to 50% were estimated to range from two to 102 subjects. Estimates of confidence that changes observed in a given patient were due to intervening therapy rather than variability of the technique were calculated to range from 71% to 87% if a 20% reduction in a parameter was observed. CONCLUSION The rate of reproducibility of DCE-MRI parameters is in the range of 10%-20% and is influenced by lesion location, parameters being significantly more reproducible in the liver than in the lung. These findings provide the foundation for interpretation of results and design of clinical trials in which DCE-MRI studies are used to assess objective responses.

A novel volumetric feature extraction technique with applications to MR images

A semiautomated feature extraction algorithm is presented for the extraction and measurement of the hippocampus from volumetric magnetic resonance imaging (MRI) head scans. This algorithm makes use of elements of both deformable model and region growing techniques and allows incorporation of a priori operator knowledge of hippocampal location and shape. Experimental results indicate that the algorithm is able to estimate hippocampal volume and asymmetry with an accuracy which approaches that of laborious manual outlining techniques.

Multialgorithm solution for automated multispectral target detection

A solution to the problem of automated detection of targets with unknown spectral properties in multispectral imagery is presented that makes use of three background characterization and suppression algorithms in series. The first, parametric Bayesian clustering, is used to accurately characterize individual elements of the background scene. The second, background suppression filtering, eliminates those dimen- sions of multispectral space containing the majority of background en- ergy. Finally, a multidimensional extension of the well-known Linde- Buzo-Gray (LBG) clustering algorithm is used to characterize what remains of the background and extract any anomalous target signatures. The results of this process are compared to spectral decorrelation (RX) filtering alone, LBG clustering alone, and RX filtering in combination with background suppression filtering. The process presented is shown to be significantly superior to each of these algorithm combinations.

Quantitative MR in multi-center clinical trials

MRI has a wide variety of applications in the clinical trials process. MR has shown particular utility in the early phases of clinical development, when trial sponsors are interested in demonstrating proof of concept and must make decisions about allocation of resources to a particular compound based on the results from a small number of experimental subjects. This utility is largely due to the many different imaging endpoints that can be measured using MR, ranging from structural (tumor burden, hippocampal volume) to functional (blood flow, vascular permeability) to molecular (hepatic fat fraction, glycosaminoglycan content). The unique flexibility of these systems has proven to be both a blessing and a curse to those attempting to deploy MR in multi‐center clinical trials, however, as differences among scanner manufacturers and models in pulse sequence implementation, hardware capabilities, and even terminology make it increasingly difficult to ensure that results obtained at one center are comparable to those at another. These problems are compounded by the differences between the procedures used in clinical trials and those used in routine clinical practice, which make trial‐specific training for site technologists and radiologists a necessity in many cases. This article will briefly review the benefits of including quantitative MR imaging in clinical trials, then explore in detail the challenges presented by the need to develop and deploy a detailed MR protocol that is both effective and implementable across many different MR systems and software versions. J. Magn. Reson. Imaging 2010; 31: 279–288.

Inter-Operator variability in perfusion assessment of tumors in MRI using automated AIF detection

A method is presented for the calculation of perfusion parameters in dynamic contrast enhanced MRI. This method requires identification of enhancement curves for both tumor tissue and plasma. Inter-operator variability in the derived rate constant between plasma and extra-cellular extra-vascular space is assessed in both canine and human subjects using semi-automated tumor margin identification with both manual and automated arterial input function (AIF) identification. Experimental results show a median coefficient of variability (CV) for parameter measurement with manual AIF identification of 21.5% in canines and 11% in humans, with a median CV for parameter measurement with automated AIF identification of 6.7% in canines and 6% in humans.