Pharmacokinetic and Metabolomic Studies with a Promising Radiation Countermeasure, BBT-059 (PEGylated interleukin-11), in Rhesus Nonhuman Primates.

BBT-059, a long-acting PEGylated interleukin-11 (IL-11) analog that is believed to have hematopoietic promoting and anti-apoptotic properties, is being developed as a potential radiation medical countermeasure (MCM) for hematopoietic acute radiation syndrome (H-ARS). This agent has been shown to improve survival in lethally irradiated mice. To further evaluate the drug's toxicity and safety profile, 12 naïve nonhuman primates (NHPs, rhesus macaques) were administered one of three doses of BBT-059 subcutaneously and were monitored for the next 21 days. Blood samples were collected throughout the study to assess the pharmacokinetics (PK) and pharmacodynamics (PD) of the drug as well as its effects on complete blood counts, cytokines, vital signs, and to conduct metabolomic studies. No adverse effects were detected in any treatment group during the study. Short-term changes in metabolomic profiles were present in all groups treated with BBT-059 beginning immediately after drug administration and reverting to near normal levels by the end of the study period. Several pathways and metabolites, particularly those related to inflammation and steroid hormone biosynthesis, were activated by BBT-059 administration. Taken together, these observations suggest that BBT-059 has a good safety profile for further development as a radiation MCM for regulatory approval for human use.

Genetic Upregulation of Activated Protein C Mitigates Delayed Effects of Acute Radiation Exposure in the Mouse Plasma.

Exposure to ionizing radiation, accidental or intentional, may lead to delayed effects of acute radiation exposure (DEARE) that manifest as injury to organ systems, including the kidney, heart, and brain. This study examines the role of activated protein C (APC), a known mitigator of radiation-induced early toxicity, in long-term plasma metabolite and lipid panels that may be associated with DEARE in APCHi mice. The APCHi mouse model used in the study was developed in a C57BL/6N background, expressing the D168F/N173K mouse analog of the hyper-activatable human D167F/D172K protein C variant. This modification enables increased circulating APC levels throughout the mouse's lifetime. Male and female cohorts of C57BL/6N wild-type and APCHi transgenic mice were exposed to 9.5 Gy γ-rays with their hind legs shielded to allow long-term survival that is necessary to monitor DEARE, and plasma was collected at 6 months for LC-MS-based metabolomics and lipidomics. We observed significant dyslipidemia, indicative of inflammatory phenotype, upon radiation exposure. Additionally, observance of several other metabolic dysregulations was suggestive of gut damage, perturbations in TriCarboxylic Acid (TCA) and urea cycles, and arginine metabolism. We also observed gender- and genotype-modulated metabolic perturbations post radiation exposure. The APCHi mice showed near-normal abundance for several lipids. Moreover, restoration of plasma levels of some metabolites, including amino acids, citric acid, and hypoxanthine, in APCHi mice is indicative of APC-mediated protection from radiation injuries. With the help of these findings, the role of APC in plasma molecular events after acute γ-radiation exposure in a gender-specific manner can be established for the first time.

Metabolomic Profiles in Tissues of Nonhuman Primates Exposed to Either Total- or Partial-Body Radiation.

A complex cascade of systemic and tissue-specific responses induced by exposure to ionizing radiation can lead to functional impairment over time in the surviving population. Current methods for management of survivors of unintentional radiation exposure episodes rely on monitoring individuals over time for the development of adverse clinical symptoms due to the lack of predictive biomarkers for tissue injury. In this study, we report on changes in metabolomic and lipidomic profiles in multiple tissues of nonhuman primates (NHPs) that received either 4.0 Gy or 5.8 Gy total-body irradiation (TBI) of 60Co gamma rays, and 4.0 or 5.8 Gy partial-body irradiation (PBI) from LINAC-derived photons and were treated with a promising radiation countermeasure, gamma-tocotrienol (GT3). These include small molecule alterations that correlate with radiation effects in the jejunum, lung, kidney, and spleen of animals that either survived or succumbed to radiation toxicities over a 30-day period. Radiation-induced metabolic changes in tissues were observed in animals exposed to both doses and types of radiation, but were partially alleviated in GT3-treated and irradiated animals, with lung and spleen being most responsive. The majority of the pathways protected by GT3 treatment in these tissues were related to glucose metabolism, inflammation, and aldarate metabolism, suggesting GT3 may exert radioprotective effects in part by sparing these pathways from radiation-induced dysregulation. Taken together, the results of our study demonstrate that the prophylactic administration of GT3 results in metabolic and lipidomic shifts that likely provide an overall advantage against radiation injury. This investigation is among the first to highlight the use of a molecular phenotyping approach in a highly translatable NHP model of partial- and total-body irradiation to determine the underlying physiological mechanisms involved in the radioprotective efficacy of GT3.

O-GlcNAcPRED-DL: Prediction of Protein O-GlcNAcylation Sites Based on an Ensemble Model of Deep Learning.

O-linked ß-N-acetylglucosamine (O-GlcNAc) is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine residues of proteins, regulating a plethora of physiological and pathological events. As a dynamic process, O-GlcNAc functions in a site-specific manner. However, the experimental identification of the O-GlcNAc sites remains challenging in many scenarios. Herein, by leveraging the recent progress in cataloguing experimentally identified O-GlcNAc sites and advanced deep learning approaches, we establish an ensemble model, O-GlcNAcPRED-DL, a deep learning-based tool, for the prediction of O-GlcNAc sites. In brief, to make a benchmark O-GlcNAc data set, we extracted the information on O-GlcNAc from the recently constructed database O-GlcNAcAtlas, which contains thousands of experimentally identified and curated O-GlcNAc sites on proteins from multiple species. To overcome the imbalance between positive and negative data sets, we selected five groups of negative data sets in humans and mice to construct an ensemble predictor based on connection of a convolutional neural network and bidirectional long short-term memory. By taking into account three types of sequence information, we constructed four network frameworks, with the systematically optimized parameters used for the models. The thorough comparison analysis on two independent data sets of humans and mice and six independent data sets from other species demonstrated remarkably increased sensitivity and accuracy of the O-GlcNAcPRED-DL models, outperforming other existing tools. Moreover, a user-friendly Web server for O-GlcNAcPRED-DL has been constructed, which is freely available at http://oglcnac.org/pred_dl.

Serum metabolomics of treatment response in myasthenia gravis.

OBJECTIVE: High-dose prednisone use, lasting several months or longer, is the primary initial therapy for myasthenia gravis (MG). Upwards of a third of patients do not respond to treatment. Currently no biomarkers can predict clinical responsiveness to corticosteroid treatment. We conducted a discovery-based study to identify treatment responsive biomarkers in MG using sera obtained at study entry to the thymectomy clinical trial (MGTX), an NIH-sponsored randomized, controlled study of thymectomy plus prednisone versus prednisone alone. METHODS: We applied ultra-performance liquid chromatography coupled with electro-spray quadrupole time of flight mass spectrometry to obtain comparative serum metabolomic and lipidomic profiles at study entry to correlate with treatment response at 6 months. Treatment response was assessed using validated outcome measures of minimal manifestation status (MMS), MG-Activities of Daily Living (MG-ADL), Quantitative MG (QMG) score, or a strictly defined composite measure of response. RESULTS: Increased serum levels of phospholipids were associated with treatment response as assessed by QMG, MMS, and the Responders classification, but all measures showed limited overlap in metabolomic profiles, in particular the MG-ADL. A panel including histidine, free fatty acid (13:0), γ-cholestenol and guanosine was highly predictive of the strictly defined treatment response measure. The AUC in Responders' prediction for these markers was 0.90 irrespective of gender, age, thymectomy or baseline prednisone use. Pathway analysis suggests that xenobiotic metabolism could play a major role in treatment resistance. There was no association with outcome and gender, age, thymectomy or baseline prednisone use. INTERPRETATION: We have defined a metabolomic and lipidomic profile that can now undergo validation as a treatment predictive marker for MG patients undergoing corticosteroid therapy. Metabolomic profiles of outcome measures had limited overlap consistent with their assessing distinct aspects of treatment response and supporting unique biological underpinning for each outcome measure. Interindividual variation in prednisone metabolism may be a determinate of how well patients respond to treatment.

Modeling of soluble solid content of PE-packaged blueberries based on near-infrared spectroscopy with back propagation neural network and partial least squares (BP-PLS) algorithm.

Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near-infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)-packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky-Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR-based SSC prediction models for unpacked blueberries and PE-packaged blueberries were developed using partial least squares (PLS). The prediction model for PE-packaged samples (RP 2 = 0.876, root mean square error of prediction [RMSEP] = 0.632) had less precision than the model for unpacked samples (RP 2 = 0.953, RMSEP = 0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP-PLS algorithm based on the residual learning algorithm. The model of BP-PLS (RP 2 = 0.947, RMSEP = 0.414) was successfully developed to improve the prediction accuracy of SSC for PE-packaged blueberries. The results suggested a promising way of using the BP-PLS method in tandem with NIR for the rapid detection of the SSC of PE-packaged blueberries. PRACTICAL APPLICATION: Most of the NIR-based research used unpacked blueberries as samples, while the use of packaged blueberries would provide researchers with a better understanding of the crucial factors at different phases of the blueberry supply chain (from harvest to sale). To meet market demands and minimize losses, NIR spectroscopy has been proven to be a rapid and nondestructive method for the determination of the SSC of PE-packaged blueberries. This study provides an effective method for monitoring the properties of blueberries in the entire supply chain.

Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction.

Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples.

Effects of proton and oxygen ion irradiation on cardiovascular function and structure in a rabbit model.

PURPOSE: Astronauts on missions beyond low Earth orbit will be exposed to galactic cosmic radiation, and there is concern about potential adverse cardiovascular effects. Most of the research to identify cardiovascular risk of space radiation has been performed in rodent models. To aid in the translation of research results to humans, the current study identified long-term effects of high-energy charged particle irradiation on cardiovascular function and structure in a larger non-rodent animal model. MATERIALS AND METHODS: At the age of 12 months, male New Zealand white rabbits were exposed to whole-body protons (250 MeV) or oxygen ions (16O, 600 MeV/n) at a dose of 0 or 0.5 Gy and were followed for 12 months after irradiation. Ultrasonography was used to measure in vivo cardiac function and blood flow parameters at 10- and 12-months post-irradiation. At 12 months after irradiation, blood cell counts and blood chemistry values were assessed, and cardiac tissue and aorta were collected for histological as well as molecular and biochemical analyses. Plasma was used for metabolomic analysis and to quantify common markers of cardiac injury. RESULTS: A small but significant decrease in the percentage of circulating lymphocytes and an increase in neutrophil percentage was seen 12 months after 0.5 Gy protons, while 16O exposure resulted in an increase in monocyte percentage. Markers of cardiac injury, cardiac troponin I (cTnI) and N-Terminal pro-B-type Natriuretic Peptide were modestly increased in the proton group, and cTnI was also increased after 16O. On the other hand, metabolomics on plasma at 12 months revealed no changes. Both types of irradiation demonstrated alterations in cardiac mitochondrial morphology and an increase in left ventricular protein levels of inflammatory cell marker CD68. However, changes in cardiac function were only mild. CONCLUSION: Low dose charged particle irradiation caused mild long-term changes in inflammatory markers, cardiac function, and structure in the rabbit heart, in line with previous studies in mouse and rat models.

Comparison of various chemometric methods on visible and near-infrared spectral analysis for wood density prediction among different tree species and geographical origins.

Visible and near-infrared (Vis-NIR) spectroscopy has been widely applied in many fields for the qualitative and quantitative analysis. Chemometric techniques including pre-processing, variable selection, and multivariate calibration models play an important role to better extract useful information from spectral data. In this study, a new de-noising method (lifting wavelet transform, LWT), four variable selection methods, as well as two non-linear machine learning models were simultaneously analyzed to compare the impact of chemometric approaches on wood density determination among various tree species and geographical locations. In addition, fruit fly optimization algorithm (FOA) and response surface methodology (RSM) were employed to optimize the parameters of generalized regression neural network (GRNN) and particle swarm optimization-support vector machine (PSO-SVM), respectively. As for various chemometric methods, the optimal chemometric method was different for the same tree species collected from different locations. FOA-GRNN model combined with LWT and CARS deliver the best performance for Chinese white poplar of Heilongjiang province. In contrast, PLS model showed a good performance for Chinese white poplar collected from Jilin province based on raw spectra. However, for other tree species, RSM-PSO-SVM models can improve the performance of wood density prediction compared to traditional linear and FOA-GRNN models. Especially for Acer mono Maxim, when compared to linear models, the coefficient of determination of prediction set ( R p 2 ) and relative prediction deviation (RPD) were increased by 47.70% and 44.48%, respectively. And the dimensionality of Vis-NIR spectral data was decreased from 2048 to 20. Therefore, the appropriate chemometric technique should be selected before building calibration models.

Analysis of the Proteomic Profile in Serum of Irradiated Nonhuman Primates Treated with Ex-Rad, a Radiation Medical Countermeasure.

There are currently four radiation medical countermeasures that have been approved by the United States Food and Drug Administration to mitigate hematopoietic acute radiation syndrome, all of which are repurposed radiomitigators. The evaluation of additional candidate drugs that may also be helpful for use during a radiological/nuclear emergency is ongoing. A chlorobenzyl sulfone derivative (organosulfur compound) known as Ex-Rad, or ON01210, is one such candidate medical countermeasure, being a novel, small-molecule kinase inhibitor that has demonstrated efficacy in the murine model. In this study, nonhuman primates exposed to ionizing radiation were subsequently administered Ex-Rad as two treatment schedules (Ex-Rad I administered 24 and 36 h post-irradiation, and Ex-Rad II administered 48 and 60 h post-irradiation) and the proteomic profiles of serum using a global molecular profiling approach were assessed. We observed that administration of Ex-Rad post-irradiation is capable of mitigating radiation-induced perturbations in protein abundance, particularly in restoring protein homeostasis, immune response, and mitigating hematopoietic damage, at least in part after acute exposure. Taken together, restoration of functionally significant pathway perturbations may serve to protect damage to vital organs and provide long-term survival benefits to the afflicted population.

Analysis of the urinary metabolic profiles in irradiated rats treated with Activated Protein C (APC), a potential mitigator of radiation toxicity.

PURPOSE: The goal of the current study was to identify longitudinal changes in urinary metabolites following IR exposure and to determine potential alleviation of radiation toxicities by administration of recombinant APC formulations. MATERIALS AND METHODS: Female adult WAG/RijCmcr rats were irradiated with 13.0 Gy leg-out partial body X-rays; longitudinally collected urine samples were subject to LC-MS based metabolomic profiling. Sub-cohorts of rats were treated with three variants of recombinant APC namely, rat wildtype (WT) APC, rat 3K3A mutant form of APC, and human WT APC as two bolus injections at 24 and 48 hours post IR. RESULTS: Radiation induced robust changes in the urinary profiles leading to oxidative stress, severe dyslipidemia, and altered biosynthesis of PUFAs, glycerophospholipids, sphingolipids, and steroids. Alterations were observed in multiple metabolic pathways related to energy metabolism, nucleotide biosynthesis and metabolism that were indicative of disrupted mitochondrial function and DNA damage. On the other hand, sub-cohorts of rats that were treated with rat wildtype-APC showed alleviation of radiation toxicities, in part, at the 90-day time point, while rat 3K3A-APC showed partial alleviation of radiation induced metabolic alterations 14 days after irradiation. CONCLUSIONS: Taken together, these results show that augmenting the Protein C pathway and activity via administration of recombinant APC may be an effective approach for mitigation of radiation induced normal tissue toxicity.

Effects of Whole and Partial Heart Irradiation on Collagen, Mast Cells, and Toll-like Receptor 4 in the Mouse Heart.

In radiation therapy of tumors in the chest, such as in lung or esophageal cancer, part of the heart may be situated in the radiation field. This can lead to the development of radiation-induced heart disease. The mechanisms by which radiation causes long-term injury to the heart are not fully understood, but investigations in pre-clinical research models can contribute to mechanistic insights. Recent developments in X-ray technology have enabled partial heart irradiation in mouse models. In this study, adult male and female C57BL/6J mice were exposed to whole heart (a single dose of 8 or 16 Gy) and partial heart irradiation (16 Gy to 40% of the heart). Plasma samples were collected at 5 days and 2 weeks after the irradiation for metabolomics analysis, and the cardiac collagen deposition, mast cell numbers, and left ventricular expression of Toll-like receptor 4 (TLR4) were examined in the irradiated and unirradiated parts of the heart at 6 months after the irradiation. Small differences were found in the plasma metabolite profiles between the groups. However, the collagen deposition did not differ between the irradiated and unirradiated parts of the heart, and radiation did not upregulate the mast cell numbers in either part of the heart. Lastly, an increase in the expression of TLR4 was seen only after a single dose of 8 Gy to the whole heart. These results suggest that adverse tissue remodeling was not different between the irradiated and unirradiated portions of the mouse heart. While there were no clear differences between male and female animals, additional work in larger cohorts may be required to confirm this result, and to test the inhibition of TLR4 as an intervention strategy in radiation-induced heart disease.

Radiotherapy Induces Innate Immune Responses in Patients Treated for Prostate Cancers.

PURPOSE: Radiotherapy is a curative therapeutic modality used to treat cancers as a single agent or in combination with surgery and chemotherapy. Advanced radiotherapy technologies enable treatment with large fractions and highly conformal radiation doses to effect free-radical damage to cellular DNA leading to cell-cycle arrest, cell death, and innate immune response (IIR) stimulation. EXPERIMENTAL DESIGN: To understand systemic clinical responses after radiation exposure, proteomic and metabolomic analyses were performed on plasma obtained from patients with cancer at intervals after prostate stereotactic body radiotherapy. Pathway and multivariate analyses were used to delineate molecular alterations following radiotherapy and its correlation with clinical outcomes. RESULTS: DNA damage response increased within the first hour after treatment and returned to baseline by 1 month. IIR signaling also increased within 1 hour of treatment but persisted for up to 3 months thereafter. Furthermore, robust IIR and metabolite elevations, consistent with an early proinflammatory M1-mediated innate immune activation, were observed in patients in remission, whereas patients experiencing prostate serum antigen-determined disease progression demonstrated less robust immune responses and M2-mediated metabolite elevations. CONCLUSIONS: To our knowledge, these data are the first report of longitudinal proteomic and metabolomic molecular responses in patients after radiotherapy for cancers. The data supports innate immune activation as a critical clinical response of patients receiving radiotherapy for prostate cancer. Furthermore, we propose that the observed IIR may be generalized to the treatment of other cancer types, potentially informing multidisciplinary therapeutic strategies for cancer treatment.

Prediction approach of larch wood density from visible-near-infrared spectroscopy based on parameter calibrating and transfer learning.

Wood density, as a key indicator to measure wood properties, is of weighty significance in enhancing wood utilization and modifying wood properties in sustainable forest management. Visible-near-infrared (Vis-NIR) spectroscopy provides a feasible and efficient solution for obtaining wood density by the advantages of its efficiency and non-destructiveness. However, the spectral responses are different in wood products with different moisture content conditions, and changes in external factors may cause the regression model to fail. Although some calibration transfer methods and convolutional neural network (CNN)-based deep transfer learning methods have been proposed, the generalization ability and prediction accuracy of the models still need to be improved. For the prediction problem of Vis-NIR wood density in different moisture contents, a deep transfer learning hybrid method with automatic calibration capability (Resnet1D-SVR-TrAdaBoost.R2) was proposed in this study. The disadvantage of overfitting was avoided when CNN processes small sample data, which considered the complex exterior factors in actual production to enhance feature extraction and migration between samples. Density prediction of the method was performed on a larch dataset with different moisture content conditions, and the hybrid method was found to achieve the best prediction results under the calibration samples with different target domain calibration samples and moisture contents, and the performance of models was better than that of the traditional calibration transfer and migration learning methods. In particular, the hybrid model has achieved an improvement of about 0.1 in both R 2 and root mean square error (RMSE) values compared to the support vector regression model transferred by piecewise direct standardization method (SVR+PDS), which has the best performance among traditional calibration methods. To further ascertain the generalizability of the hybrid model, the model was validated with samples collected from mixed moisture contents as the target domain. Various experiments demonstrated that the Resnet1D-SVR-TrAdaBoost.R2 model could predict larch wood density with a high generalization ability and accuracy effectively but was computation consuming. It showed the potential to be extended to predict other metrics of wood.

HexNAcQuest: A Tool to Distinguish O-GlcNAc and O-GalNAc.

Protein glycosylation plays crucial roles in the regulation of diverse biological processes. As a critical step, mass spectrometry-based site-specific analysis of protein glycosylation is important to better understand these events. Despite the great progress, characterization of structural isomers of glycans and glycopeptides remains challenging. In typical glycoproteomic analysis, collision-induced dissociation (CID) or higher-energy collisional dissociation (HCD) fragmentation produces abundant saccharide oxonium ions containing N-acetylhexosamine (HexNAc) residues. However, it has been difficult to distinguish isobaric GalNAc and GlcNAc modifications by using mass spectrometry only. By using intensities of oxonium ions of standard O-GlcNAc/O-GalNAc peptides, we systematically investigated the fragmentation patterns of different ions. Then a binary logistic regression model was established by training comprehensive data sets from glycoproteomics studies reported. The model was then tested with independent O-glycoproteomics data sets, with reliable classification achieved (>87% accuracy). In comparison to empirical observations and criteria used previously, our model is accurate and generalized. Based on this model, a corresponding Web server HexNAcQuest has been constructed, which is freely accessible to users. The model can also be easily integrated in MS-based glycoproteomics workflows to distinguish the isobaric HexNAc modifications.

Pharmacokinetic and metabolomic studies with a BIO 300 Oral Powder formulation in nonhuman primates.

BIO 300, a pharmaceutical formulation of genistein, is being developed as a radiation countermeasure to treat hematopoietic acute radiation syndrome (H-ARS) and the delayed effects of acute radiation exposure (DEARE). Several studies have affirmed its safety and efficacy in alleviating the damaging effects of ionizing radiation. However, dose optimization of any drug has always been an important area of research because unnecessarily high drug doses may result in serious complications. In this study, we assessed the pharmacokinetics (PK) and metabolic profiles of two different doses of a novel solid-dosage formulation of BIO 300 (BIO 300 Oral Powder; 100 mg/kg and 200 mg/kg), when administered orally to nonhuman primates (NHPs). While the Tmax values of both doses remained the same, the area under the curve at 48 h (AUC0-48) was tripled by doubling the dose. Additionally, we monitored serum samples for global metabolomic/lipidomic changes using high resolution mass spectrometry followed by functional pathway analysis prior to and at various time points up to 48 h post drug administration. Interestingly, the metabolomic profiles of sera from NHPs that received the lower dose demonstrated a transient perturbation in numerous metabolites between the 4 and 12 h time points. Eventually, the metabolite abundance reverted to near-normal by 48 h. These study results are consistent with our previous studies focused on the PK and metabolomic analysis for parenteral and oral aqueous nanosuspension formulations of BIO 300. This study affirms that administration of a single dose of up to 200 mg/kg of BIO 300 Oral Powder is safe in NHPs and conferred no metabolomic-mediated safety features.

Identification of Novel Biomarkers for Acute Radiation Injury Using Multiomics Approach and Nonhuman Primate Model.

PURPOSE: The availability of validated biomarkers to assess radiation exposure and to assist in developing medical countermeasures remains an unmet need. METHODS AND MATERIALS: We used a cobalt-60 γ-irradiated nonhuman primate (NHP) model to delineate a multiomics-based serum probability index of radiation exposure. Both male and female NHPs were irradiated with different doses ranging from 6.0 to 8.5 Gy, with 0.5 Gy increments between doses. We leveraged high-resolution mass spectrometry for analysis of metabolites, lipids, and proteins at 1, 2, and 6 days postirradiation in NHP serum. RESULTS: A logistic regression model was implemented to develop a 4-analyte panel to stratify irradiated NHPs from unirradiated with high accuracy that was agnostic for all doses of γ-rays tested in the study, up to 6 days after exposure. This panel was comprised of Serpin family A9, acetylcarnitine, glycerophosphocholine (16:0/22:6), and suberylglycine, which showed 2- to 4-fold elevation in serum abundance upon irradiation in NHPs and can potentially be translated as a molecular diagnostic for human use after larger validation studies. CONCLUSIONS: Taken together, this study, for the first time, demonstrates the utility of a combinatorial molecular characterization approach using an NHP model for developing minimally invasive assays from small volumes of blood that can be effectively used for radiation exposure assessments.

A multi-omics approach identifies pancreatic cancer cell extracellular vesicles as mediators of the unfolded protein response in normal pancreatic epithelial cells.

Although cancer-derived extracellular vesicles (cEVs) are thought to play a pivotal role in promoting cancer progression events, their precise effect on neighbouring normal cells is unknown. In this study, we investigated the impact of pancreatic cancer ductal adenocarcinoma (PDAC) derived EVs on recipient non-tumourigenic pancreatic normal epithelial cells upon internalization. We demonstrate that cEVs are readily internalized and induce endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) in treated normal pancreatic epithelial cells within 24 h. We further show that PDAC cEVs increase cell proliferation, migration, and invasion and that these changes are regulated at least in part, by the UPR mediator DDIT3. Subsequently, these cells release several inflammatory cytokines. Leveraging a layered multi-omics approach, we analysed EV cargo from a panel of six PDAC and two normal pancreas cell lines, using multiple EV isolation methods. We found that cEVs were enriched for an array of biomolecules which can induce or regulate ER stress and the UPR, including palmitic acid, sphingomyelins, metabolic regulators of tRNA charging and proteins which regulate trafficking and degradation. We further show that palmitic acid, at doses relevant to those found in cEVs, is sufficient to induce ER stress in normal pancreas cells. These results suggest that cEV cargo packaging may be designed to disseminate proliferative and invasive characteristics upon internalization by distant recipient normal cells, hitherto unreported. This study is among the first to highlight a major role for PDAC cEVs to induce stress in treated normal pancreas cells that may modulate a systemic response leading to altered phenotypes. These findings highlight the importance of EVs in mediating disease aetiology and open potential areas of investigation toward understanding the role of cEV lipids in promoting cell transformation in the surrounding microenvironment.

An optimized method for the isolation of urinary extracellular vesicles for molecular phenotyping: detection of biomarkers for radiation exposure.

BACKGROUND: Urinary extracellular vesicles (EVs) are a source of biomarkers with broad potential applications across clinical research, including monitoring radiation exposure. A key limitation to their implementation is minimal standardization in EV isolation and analytical methods. Further, most urinary EV isolation protocols necessitate large volumes of sample. This study aimed to compare and optimize isolation and analytical methods for EVs from small volumes of urine. METHODS: 3 EV isolation methods were compared: ultracentrifugation, magnetic bead-based, and size-exclusion chromatography from 0.5 mL or 1 mL of rat and human urine. EV yield and mass spectrometry signals (Q-ToF and Triple Quad) were evaluated from each method. Metabolomic profiling was performed on EVs isolated from the urine of rats exposed to ionizing radiation 1-, 14-, 30- or 90-days post-exposure, and human urine from patients receiving thoracic radiotherapy for the treatment of lung cancer pre- and post-treatment. RESULTS: Size-exclusion chromatography is the preferred method for EV isolation from 0.5 mL of urine. Mass spectrometry-based metabolomic analyses of EV cargo identified biochemical changes induced by radiation, including altered nucleotide, folate, and lipid metabolism. We have provided standard operating procedures for implementation of these methods in other laboratories. CONCLUSIONS: We demonstrate that EVs can be isolated from small volumes of urine and analytically investigated for their biochemical contents to detect radiation induced metabolomic changes. These findings lay a groundwork for future development of methods to monitor response to radiotherapy and can be extended to an array of molecular phenotyping studies aimed at characterizing EV cargo.

Safety and Pharmacokinetic Assessment of Oral Proglumide in Those with Hepatic Impairment.

Proglumide is an orally administered cholecystokinin receptor antagonist that was found to improve nonalcoholic steatohepatitis, reverse liver fibrosis, and decrease incidence of hepatocellular carcinoma (HCC) in animal models. The current investigation aimed to test the pharmacokinetics and safety of proglumide in subjects with hepatic impairment compared with healthy controls. In this translational study, subjects with confirmed cirrhosis, Child-Pugh stage A or B, or healthy controls were recruited for a single-dosing study. Baseline urine and blood samples were obtained before administration of proglumide and also collected after ingestion up to 24 h. Drug concentrations measured by mass spectroscopy revealed peak plasma concentrations (Cmax) of 7847, 9721, and 10,635 ng/mL at about 1 h (Tmax) for healthy controls, subjects with Child-Pugh A, and B cirrhosis, respectively. The serum elimination half time was 3 h. Maximum urine drug concentration (Cmax = ~411 µg/mL) was observed at 3 h, and urinary drug concentration declined at 5 h. There were no adverse events reported, and follow-up liver panels in cirrhosis subjects were unchanged or improved. This investigation demonstrated that proglumide is safe and has similar pharmacokinetic properties in subjects with cirrhosis as in healthy controls; therefore, it will be safe to test the efficacy of proglumide as a therapeutic agent in those subjects with cirrhosis or HCC.