Evaluations of an Early Change in Tumor Pathophysiology in Response to Radiotherapy with Oxygen Enhanced Electron Paramagnetic Resonance Imaging (OE EPRI).

PURPOSE: Electron Paramagnetic Resonance Imaging (EPRI) can image the partial pressure of oxygen (pO2) within in vivo tumor models. We sought to develop Oxygen Enhanced (OE) EPRI that measures tumor pO2 with breathing gases of 21% O2 (pO221%) and 100% O2 (pO2100%), and the differences in pO2 between breathing gases (ΔpO2). We applied OE EPRI to study the early change in tumor pathophysiology in response to radiotherapy in two tumor models of pancreatic cancer. PROCEDURES: We developed a protocol that intraperitoneally administered OX071, a trityl radical contrast agent, and then acquired anatomical MR images to localize the tumor. Subsequently, we acquired two pO221% and two pO2100% maps using the T1 relaxation time of OX071 measured with EPRI and a R1-pO2 calibration of OX071. We studied 4T1 flank tumor model to evaluate the repeatability of OE EPRI. We then applied OE EPRI to study COLO 357 and Su.86.86 flank tumor models treated with 10 Gy radiotherapy. RESULTS: The repeatability of mean pO2 for individual tumors was ± 2.6 Torr between successive scans when breathing 21% O2 or 100% O2, representing a precision of 9.6%. Tumor pO221% and pO2100% decreased after radiotherapy for both models, although the decreases were not significant or only moderately significant, and the effect sizes were modest. For comparison, ΔpO2 showed a large, highly significant decrease after radiotherapy, and the effect size was large. MANOVA and analyses of the HF10 hypoxia fraction provided similar results. CONCLUSIONS: EPRI can evaluate tumor pO2 with outstanding precision relative to other imaging modalities. The change in ΔpO2 before vs. after treatment was the best parameter for measuring the early change in tumor pathophysiology in response to radiotherapy. Our studies have established ΔpO2 from OE EPRI as a new parameter, and have established that OE EPRI is a valuable new methodology for molecular imaging.

A machine learning approach that measures pH using acidoCEST MRI of iopamidol.

Tumor acidosis is an important biomarker for aggressive tumors, and extracellular pH (pHe) of the tumor microenvironment can be used to predict and evaluate tumor responses to chemotherapy and immunotherapy. AcidoCEST MRI measures tumor pHe by exploiting the pH-dependent chemical exchange saturation transfer (CEST) effect of iopamidol, an exogenous CT agent repurposed for CEST MRI. However, all pH fitting methodologies for acidoCEST MRI data have limitations. Herein we present results of the application of machine learning for extracting pH values from CEST Z-spectra of iopamidol. We acquired 36,000 experimental CEST spectra from 200 phantoms of iopamidol prepared at five concentrations, five T1 values, and eight pH values at five temperatures, acquired at six saturation powers and six saturation times. We also acquired T1 , T2 , B1 RF power, and B0 magnetic field strength supplementary MR information. These MR images were used to train and validate machine learning models for the tasks of pH classification and pH regression. Specifically, we tested the L1-penalized logistic regression classification (LRC) model and the random forest classification (RFC) model for classifying the CEST Z-spectra for thresholds at pH 6.5 and 7.0. Our results showed that both RFC and LRC were effective for pH classification, although the RFC model achieved higher predictive value, and improved the accuracy of classification accuracy with CEST Z-spectra with a more limited set of saturation frequencies. Furthermore, we used LASSO and random forest regression (RFR) models to explore pH regression, which showed that the RFR model achieved higher accuracy and precision for estimating pH across the entire pH range of 6.2-7.3, especially when using a more limited set of features. Based on these results, machine learning for analysis of acidoCEST MRI is promising for eventual in vivo determination of tumor pHe.

Simultaneous Evaluations of pH and Enzyme Activity with a CEST MRI Contrast Agent.

The extracellular tumor microenvironment of many solid tumors has high acidosis and high protease activity. Simultaneously assessing both characteristics may improve diagnostic evaluations of aggressive tumors and the effects of anticancer treatments. Noninvasive imaging methods have previously been developed that measure extracellular pH or can detect enzyme activity using chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI). Herein, we developed a single-hybrid CEST agent that can simultaneously measure pH and evaluate protease activity using a combination of dual-power acidoCEST MRI and catalyCEST MRI. Our agent showed CEST signals at 9.2 ppm from a salicylic acid moiety and at 5.0 ppm from an aryl amide. The CEST signal at 9.2 ppm could be measured after selective saturation was applied at 1 and 4 µT, and these measurements could be used with a ratiometric analysis to determine pH. The CEST signal at 5.0 ppm from the aryl amide disappeared after the agent was treated with cathepsin B, while the CEST signal at 9.2 ppm remained, indicating that the agent could detect protease activity through the amide bond cleavage. Michaelis-Menten kinetics studies with catalyCEST MRI demonstrated that the binding affinity (as shown with the Michaelis constant KM), the catalytic turnover rate (kcat), and catalytic efficiency (kcat/KM) were each higher for cathepsin B at lower pH. The kcat rates measured with catalyCEST MRI were lower than the comparable rates measured with liquid chromatography-mass spectrometry (LC-MS), which reflected a limitation of inherently noisy and relatively insensitive CEST MRI analyses. Although this level of precision limited catalyCEST MRI to semiquantitative evaluations, these semiquantitative assessments of high and low protease activity still had value by demonstrating that high acidosis and high protease activity can be used as synergistic, multiparametric biomarkers.

Hydrogen-Bond Networks near Supported Lipid Bilayers from Vibrational Sum Frequency Generation Experiments and Atomistic Simulations.

We report vibrational sum frequency generation (SFG) spectra in which the C-H stretches of lipid alkyl tails in fully hydrogenated single- and dual-component supported lipid bilayers are detected along with the O-H stretching continuum above the bilayer. As the salt concentration is increased from ∼10 µM to 0.1 M, the SFG intensities in the O-H stretching region decrease by a factor of 2, consistent with significant absorptive-dispersive mixing between χ(2) and χ(3) contributions to the SFG signal generation process from charged interfaces. A method for estimating the surface potential from the second-order spectral lineshapes (in the OH stretching region) is presented and discussed in the context of choosing truly zero-potential reference states. Aided by atomistic simulations, we find that the strength and orientation distribution of the hydrogen bonds over the purely zwitterionic bilayers are largely invariant between submicromolar and hundreds of millimolar concentrations. However, specific interactions between water molecules and lipid headgroups are observed upon replacing phosphocholine (PC) lipids with negatively charged phosphoglycerol (PG) lipids, which coincides with SFG signal intensity reductions in the 3100-3200 cm-1 frequency region. The atomistic simulations show that this outcome is consistent with a small, albeit statistically significant, decrease in the number of water molecules adjacent to both the lipid phosphate and choline moieties per unit area, supporting the SFG observations. Ultimately, the ability to probe hydrogen-bond networks over lipid bilayers holds the promise of opening paths for understanding, controlling, and predicting specific and nonspecific interactions between membranes and ions, small molecules, peptides, polycations, proteins, and coated and uncoated nanomaterials.