Pulmonary Fibrosis Diagnosis and Disease Progression Detected Via Hair Metabolome Analysis.

BACKGROUND: Fibrotic interstitial lung disease is often identified late due to non-specific symptoms, inadequate access to specialist care, and clinical unawareness precluding proper and timely treatment. Biopsy histological analysis is definitive but rarely performed due to its invasiveness. Diagnosis typically relies on high-resolution computed tomography, while disease progression is evaluated via frequent pulmonary function testing. This study tested the hypothesis that pulmonary fibrosis diagnosis and progression could be non-invasively and accurately evaluated from the hair metabolome, with the longer-term goal to minimize patient discomfort. METHODS: Hair specimens collected from pulmonary fibrosis patients (n = 56) and healthy subjects (n = 14) were processed for metabolite extraction using 2DLC/MS-MS, and data were analyzed via machine learning. Metabolomic data were used to train machine learning classification models tuned via a rigorous combination of cross validation, feature selection, and testing with a hold-out dataset to evaluate classifications of diseased vs. healthy subjects and stable vs. progressed disease. RESULTS: Prediction of pulmonary fibrosis vs. healthy achieved AUROCTRAIN = 0.888 (0.794-0.982) and AUROCTEST = 0.908, while prediction of stable vs. progressed disease achieved AUROCTRAIN = 0.833 (0.784 - 0.882) and AUROCTEST = 0. 799. Top metabolites for diagnosis included ornithine, 4-(methylnitrosamino)-1-3-pyridyl-N-oxide-1-butanol, Thr-Phe, desthiobiotin, and proline. Top metabolites for progression included azelaic acid, Thr-Phe, Ala-Tyr, indoleacetyl glutamic acid, and cytidine. CONCLUSION: This study provides novel evidence that pulmonary fibrosis diagnosis and progression may in principle be evaluated from the hair metabolome. Longer term, this approach may facilitate non-invasive and accurate detection and monitoring of fibrotic lung diseases.

Identification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data.

BACKGROUND: Diagnosis of idiopathic pulmonary fibrosis (IPF) typically relies on high-resolution computed tomography imaging (HRCT) or histopathology, while monitoring disease severity is done via frequent pulmonary function testing (PFT). More reliable and convenient methods of diagnosing fibrotic interstitial lung disease (ILD) type and monitoring severity would allow for early identification and enhance current therapeutic interventions. This study tested the hypothesis that a machine learning (ML) ensemble analysis of comprehensive metabolic panel (CMP) and complete blood count (CBC) data can accurately distinguish IPF from connective tissue disease ILD (CTD-ILD) and predict disease severity as seen with PFT. METHODS: Outpatient data with diagnosis of IPF or CTD-ILD (n = 103 visits by 53 patients) were analyzed via ML methodology to evaluate (1) IPF vs CTD-ILD diagnosis; (2) %predicted Diffusing Capacity of Lung for Carbon Monoxide (DLCO) moderate or mild vs severe; (3) %predicted Forced Vital Capacity (FVC) moderate or mild vs severe; and (4) %predicted FVC mild vs moderate or severe. RESULTS: ML methodology identified IPF from CTD-ILD with AUCTEST = 0.893, while PFT was classified as DLCO moderate or mild vs severe with AUCTEST = 0.749, FVC moderate or mild vs severe with AUCTEST = 0.741, and FVC mild vs moderate or severe with AUCTEST = 0.739. Key features included albumin, alanine transaminase, %lymphocytes, hemoglobin, %eosinophils, white blood cell count, %monocytes, and %neutrophils. CONCLUSION: Analysis of CMP and CBC data via proposed ML methodology offers the potential to distinguish IPF from CTD-ILD and predict severity on associated PFT with accuracy that meets or exceeds current clinical practice.

Anti-tumor effect of pH-sensitive drug-loaded nanoparticles optimized via an integrated computational/experimental approach.

The acidic pH of tumor tissue has been used to trigger drug release from nanoparticles. However, dynamic interactions between tumor pH and vascularity present challenges to optimize therapy to particular microenvironment conditions. Despite recent development of pH-sensitive nanomaterials that can accurately quantify drug release from nanoparticles, tailoring release to maximize tumor response remains elusive. This study hypothesizes that a computational modeling-based platform that simulates the heterogeneously vascularized tumor microenvironment can enable evaluation of the complex intra-tumoral dynamics involving nanoparticle transport and pH-dependent drug release, and predict optimal nanoparticle parameters to maximize the response. To this end, SPNCD nanoparticles comprising superparamagnetic cores of iron oxide (Fe3O4) and a poly(lactide-co-glycolide acid) shell loaded with doxorubicin (DOX) were fabricated. Drug release was measured in vitro as a function of pH. A 2D model of vascularized tumor growth was calibrated to experimental data and used to evaluate SPNCD effect as a function of drug release rate and tissue vascular heterogeneity. Simulations show that pH-dependent drug release from SPNCD delays tumor regrowth more than DOX alone across all levels of vascular heterogeneity, and that SPNCD significantly inhibit tumor radius over time compared to systemic DOX. The minimum tumor radius forecast by the model was comparable to previous in vivo SPNCD inhibition data. Sensitivity analyses of the SPNCD pH-dependent drug release rate indicate that slower rates are more inhibitory than faster rates. We conclude that an integrated computational and experimental approach enables tailoring drug release by pH-responsive nanomaterials to maximize the tumor response.

Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath.

BACKGROUND: Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity. METHODS: Exhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe). RESULTS: The proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity. CONCLUSIONS: Exhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

A clinical marker-based modeling framework to preoperatively predict lymph node and vascular space involvement in endometrial cancer patients.

INTRODUCTION: Endometrial cancer (EC) has high mortality at advanced stages. Poor prognostic factors include grade 3 tumors, deep myometrial invasion, lymph node metastasis (LNM), and lymphovascular space invasion (LVSI). Preoperative knowledge of patients at higher risk of lymph node involvement, when such involvement is not suspected, would benefit surgery planning and patient prognosis. This study implements an ensemble machine learning approach that evaluates Cancer Antigen 125 (CA125) along with histologic type, preoperative grade, and age to predict LVSI, LNM and stage in EC patients. METHODS: A retrospective chart review spanning January 2000 to January 2015 at a regional hospital was performed. Women 18 years or older with a diagnosis of EC and preoperative or within one-week CA125 measurement were included (n = 842). An ensemble machine learning approach was implemented based on a stacked generalization technique to evaluate CA125 in combination with histologic type, preoperative grade, and age as predictors, and LVSI, LNM and disease stage as outcomes. RESULTS: The ensemble approach predicted LNM and LVSI in EC patients with AUROCTEST of 0.857 and 0.750, respectively, and predicted disease stage with AUROCTEST of 0.665. The approach achieved AUROCTEST for LVSI and LNM of 0.750 and 0.643 for grade 1 patients, and of 0.689 and 0.952 for grade 2 patients, respectively. CONCLUSION: An ensemble machine learning approach offers the potential to preoperatively predict LVSI, LNM and stage in EC patients with adequate accuracy based on CA125, histologic type, preoperative grade, and age.

Metabolomic differentiation of tumor core versus edge in glioma.

OBJECTIVE: Gliomas exhibit high intratumor and interpatient heterogeneity. Recently, it has been shown that the microenvironment and phenotype differ significantly between the glioma core (inner) and edge (infiltrating) regions. This proof-of-concept study differentiates metabolic signatures associated with these regions, with the potential for prognosis and targeted therapy that could improve surgical outcomes. METHODS: Paired glioma core and infiltrating edge samples were obtained from 27 patients after craniotomy. Liquid-liquid metabolite extraction was performed on the samples and metabolomic data were obtained via 2D liquid chromatography-mass spectrometry/mass spectrometry. To gauge the potential of metabolomics to identify clinically relevant predictors of survival from tumor core versus edge tissues, a boosted generalized linear machine learning model was used to predict metabolomic profiles associated with O6-methylguanine DNA methyltransferase (MGMT) promoter methylation. RESULTS: A panel of 66 (of 168) metabolites was found to significantly differ between glioma core and edge regions (p ≤ 0.05). Top metabolites with significantly different relative abundances included DL-alanine, creatine, cystathionine, nicotinamide, and D-pantothenic acid. Significant metabolic pathways identified by quantitative enrichment analysis included glycerophospholipid metabolism; butanoate metabolism; cysteine and methionine metabolism; glycine, serine, alanine, and threonine metabolism; purine metabolism; nicotinate and nicotinamide metabolism; and pantothenate and coenzyme A biosynthesis. The machine learning model using 4 key metabolites each within core and edge tissue specimens predicted MGMT promoter methylation status, with AUROCEdge = 0.960 and AUROCCore = 0.941. Top metabolites associated with MGMT status in the core samples included hydroxyhexanoycarnitine, spermine, succinic anhydride, and pantothenic acid, and in the edge samples metabolites included 5-cytidine monophosphate, pantothenic acid, itaconic acid, and uridine. CONCLUSIONS: Key metabolic differences are identified between core and edge tissue in glioma and, furthermore, demonstrate the potential for machine learning to provide insight into potential prognostic and therapeutic targets.

Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study.

BACKGROUND: The application of machine learning (ML) to analyze clinical data with the goal to predict patient outcomes has garnered increasing attention. Ensemble learning has been used in conjunction with ML to improve predictive performance. Although stacked generalization (stacking), a type of heterogeneous ensemble of ML models, has emerged in clinical data analysis, it remains unclear how to define the best model combinations for strong predictive performance. This study develops a methodology to evaluate the performance of "base" learner models and their optimized combination using "meta" learner models in stacked ensembles to accurately assess performance in the context of clinical outcomes. METHODS: De-identified COVID-19 data was obtained from the University of Louisville Hospital, where a retrospective chart review was performed from March 2020 to November 2021. Three differently-sized subsets using features from the overall dataset were chosen to train and evaluate ensemble classification performance. The number of base learners chosen from several algorithm families coupled with a complementary meta learner was varied from a minimum of 2 to a maximum of 8. Predictive performance of these combinations was evaluated in terms of mortality and severe cardiac event outcomes using area-under-the-receiver-operating-characteristic (AUROC), F1, balanced accuracy, and kappa. RESULTS: The results highlight the potential to accurately predict clinical outcomes, such as severe cardiac events with COVID-19, from routinely acquired in-hospital patient data. Meta learners Generalized Linear Model (GLM), Multi-Layer Perceptron (MLP), and Partial Least Squares (PLS) had the highest AUROC for both outcomes, while K-Nearest Neighbors (KNN) had the lowest. Performance trended lower in the training set as the number of features increased, and exhibited less variance in both training and validation across all feature subsets as the number of base learners increased. CONCLUSION: This study offers a methodology to robustly evaluate ensemble ML performance when analyzing clinical data.

Sex-based differences of antioxidant enzyme nanoparticle effects following traumatic brain injury.

Following traumatic brain injury (TBI), reactive oxygen species (ROS) are released in excess, causing oxidative stress, carbonyl stress, and cell death, which induce the additional release of ROS. The limited accumulation and retention of small molecule antioxidants commonly used in clinical trials likely limit the target engagement and therapeutic effect in reducing secondary injury. Small molecule drugs also need to be administered every several hours to maintain bioavailability in the brain. Therefore, there is a need for a burst and sustained release system with high accumulation and retention in the injured brain. Here, we utilized Pro-NP™ with a size of 200 nm, which was designed to have a burst and sustained release of encapsulated antioxidants, Cu/Zn superoxide dismutase (SOD1) and catalase (CAT), to scavenge ROS for >24 h post-injection. Here, we utilized a controlled cortical impact (CCI) mouse model of TBI and found the accumulation of Pro-NP™ in the brain lesion was highest when injected immediately after injury, with a reduction in the accumulation with delayed administration of 1 h or more post-injury. Pro-NP™ treatment with 9000 U/kg SOD1 and 9800 U/kg CAT gave the highest reduction in ROS in both male and female mice. We found that Pro-NP™ treatment was effective in reducing carbonyl stress and necrosis at 1 d post-injury in the contralateral hemisphere in male mice, which showed a similar trend to untreated female mice. Although we found that male and female mice similarly benefit from Pro-NP™ treatment in reducing ROS levels 4 h post-injury, Pro-NP™ treatment did not significantly affect markers of post-traumatic oxidative stress in female CCI mice as compared to male CCI mice. These findings of protection by Pro-NP™ in male mice did not extend to 7 d post-injury, which suggests subsequent treatments with Pro-NP™ may be needed to afford protection into the chronic phase of injury. Overall, these different treatment effects of Pro-NP™ between male and female mice suggest important sex-based differences in response to antioxidant nanoparticle delivery and that there may exist a maximal benefit from local antioxidant activity in injured brain.

Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling.

The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.

Antioxidant theranostic copolymer-mediated reduction in oxidative stress following traumatic brain injury improves outcome in a mouse model.

Following a traumatic brain injury (TBI), excess reactive oxygen species (ROS) and lipid peroxidation products (LPOx) are generated and lead to secondary injury beyond the primary insult. A major limitation of current treatments is poor target engagement, which has prevented success in clinical trials. Thus, nanoparticle-based treatments have received recent attention because of their ability to increase accumulation and retention in damaged brain. Theranostic neuroprotective copolymers (NPC3) containing thiol functional groups can neutralize ROS and LPOx. Immediate administration of NPC3 following injury in a controlled cortical impact (CCI) mouse model provides a therapeutic window in reducing ROS levels at 2.08-20.83 mg/kg in males and 5.52-27.62 mg/kg in females. This NPC3-mediated reduction in oxidative stress improves spatial learning and memory in males, while females show minimal improvement. Notably, NPC3-mediated reduction in oxidative stress prevents the bilateral spread of necrosis in male mice, which was not observed in female mice and likely accounts for the sex-based spatial learning and memory differences. Overall, these findings suggest sex-based differences to oxidative stress scavenger nanoparticle treatments, and a possible upper threshold of antioxidant activity that provides therapeutic benefit in injured brain since female mice benefit from NPC3 treatment to a lesser extent than male mice.

CA125 as a predictor of endometrial cancer lymphovascular space invasion and lymph node metastasis for risk stratification in the preoperative setting.

Endometrial cancer (EC) is the most common malignancy of the female reproductive system. Cancer antigen 125 (CA125) is a serum tumor marker widely reported in EC patients, particularly those with poor prognostic factors such as grade 3 tumors, deep myometrial invasion, lymph node metastasis (LNM), and extra-uterine disease. This retrospective study stratifies pre-operative CA125 levels to evaluate odds ratios (OR) and relative risk (RR) between CA125 levels and the likelihood of +LNM, lymphovascular space invasion (LVSI), grade, and stage. Patient charts for women 18 years or older with a diagnosis of EC and pre-operative or within one week CA125 measurement from January 2000 to January 2015 at a regional hospital were reviewed. OR and RR were determined by unconditional maximum likelihood estimation for CA125 levels as the predictor with staging, grade, +LVSI and +LNM as outcomes. The largest increase in risk for patients having stage I/II/III disease was 52% greater (1.52-fold risk) while largest increase in risk for patients having stage III/IV disease was 67% greater (1.67-fold risk), both at CA125 ≥ 222U/ml. Patients with CA125 ≥ 122U/ml had significantly increased risk of +LNM, with maximum increase in risk of 98% (1.98-fold risk) at 222U/ml. Patients with CA125 ≥ 175U/ml had significantly increased risk of +LVSI, with maximum increase in risk of 39% (1.39-fold risk) at 222U/ml. This study shows that elevated CA125 levels correspond to increased stage, +LVSI, and +LNM in patients with EC.

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data.

INTRODUCTION: While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value. OBJECTIVES: Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies. METHODS: Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility. RESULTS: Overall survival (OS) is predicted in external validation cohort with AUROCTEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROCTEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased. CONCLUSION: This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.

Prediction of lung cancer immunotherapy response via machine learning analysis of immune cell lineage and surface markers.

BACKGROUND: Although advances have been made in cancer immunotherapy, patient benefits remain elusive. For non-small cell lung cancer (NSCLC), monoclonal antibodies targeting programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) have shown survival benefit compared to chemotherapy. Personalization of treatment would be facilitated by a priori identification of patients likely to benefit. OBJECTIVE: This pilot study applied a suite of machine learning methods to analyze mass cytometry data of immune cell lineage and surface markers from blood samples of a small cohort (n= 13) treated with Pembrolizumab, Atezolizumab, Durvalumab, or Nivolumab as monotherapy. METHODS: Four different comparisons were evaluated between data collected at an initial visit (baseline), after 12-weeks of immunotherapy, and from healthy (control) samples: healthy vs patients at baseline, Responders vs Non-Responders at baseline, Healthy vs 12-week Responders, and Responders vs Non-Responders at 12-weeks. The algorithms Random Forest, Partial Least Squares Discriminant Analysis, Multi-Layer Perceptron, and Elastic Net were applied to find features differentiating between these groups and provide for the capability to predict outcomes. RESULTS: Particular combinations and proportions of immune cell lineage and surface markers were sufficient to accurately discriminate between the groups without overfitting the data. In particular, markers associated with the B-cell phenotype were identified as key features. CONCLUSIONS: This study illustrates a comprehensive machine learning analysis of circulating immune cell characteristics of NSCLC patients with the potential to predict response to immunotherapy. Upon further evaluation in a larger cohort, the proposed methodology could help guide personalized treatment selection in clinical practice.

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival.

INTRODUCTION: Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive. OBJECTIVES: This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS. METHODS: Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events. RESULTS: Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates). CONCLUSION: Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

Theranostic Copolymers Neutralize Reactive Oxygen Species and Lipid Peroxidation Products for the Combined Treatment of Traumatic Brain Injury.

Traumatic brain injury (TBI) results in the generation of reactive oxygen species (ROS) and lipid peroxidation product (LPOx), including acrolein and 4-hydroxynonenal (4HNE). The presence of these biochemical derangements results in neurodegeneration during the secondary phase of the injury. The ability to rapidly neutralize multiple species could significantly improve outcomes for TBI patients. However, the difficulty in creating therapies that target multiple biochemical derangements simultaneously has greatly limited therapeutic efficacy. Therefore, our goal was to design a material that could rapidly bind and neutralize both ROS and LPOx following TBI. To do this, a series of thiol-functionalized biocompatible copolymers based on lipoic acid methacrylate and polyethylene glycol monomethyl ether methacrylate (FW ∼ 950 Da) (O950) were prepared. A polymerizable gadolinium-DOTA methacrylate monomer (Gd-MA) was also synthesized starting from cyclen to facilitate direct magnetic resonance imaging and in vivo tracking of accumulation. These neuroprotective copolymers (NPCs) were shown to rapidly and effectively neutralize both ROS and LPOx. Horseradish peroxidase absorbance assays showed that the NPCs efficiently neutralized H2O2, while R-phycoerythrin protection assays demonstrated their ability to protect the fluorescent protein from oxidative damage. 1H NMR studies indicated that the thiol-functional NPCs rapidly form covalent bonds with acrolein, efficiently removing it from solution. In vitro cell studies with SH-SY5Y-differentiated neurons showed that NPCs provide unique protection against toxic concentrations of both H2O2 and acrolein. NPCs rapidly accumulate and are retained in the injured brain in controlled cortical impact mice and reduce post-traumatic oxidative stress. Therefore, these materials show promise for improved target engagement of multiple biochemical derangements in hopes of improving TBI therapeutic outcomes.

Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data.

Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.

Smooth muscle cells affect differential nanoparticle accumulation in disturbed blood flow-induced murine atherosclerosis.

Atherosclerosis is a lipid-driven chronic inflammatory disease that leads to the formation of plaques in the inner lining of arteries. Plaques form over a range of phenotypes, the most severe of which is vulnerable to rupture and causes most of the clinically significant events. In this study, we evaluated the efficacy of nanoparticles (NPs) to differentiate between two plaque phenotypes based on accumulation kinetics in a mouse model of atherosclerosis. This model uses a perivascular cuff to induce two regions of disturbed wall shear stress (WSS) on the inner lining of the instrumented artery, low (upstream) and multidirectional (downstream), which, in turn, cause the development of an unstable and stable plaque phenotype, respectively. To evaluate the influence of each WSS condition, in addition to the final plaque phenotype, in determining NP uptake, mice were injected with NPs at intermediate and fully developed stages of plaque growth. The kinetics of artery wall uptake were assessed in vivo using dynamic contrast-enhanced magnetic resonance imaging. At the intermediate stage, there was no difference in NP uptake between the two WSS conditions, although both were different from the control arteries. At the fully-developed stage, however, NP uptake was reduced in plaques induced by low WSS, but not multidirectional WSS. Histological evaluation of plaques induced by low WSS revealed a significant inverse correlation between the presence of smooth muscle cells and NP accumulation, particularly at the plaque-lumen interface, which did not exist with other constituents (lipid and collagen) and was not present in plaques induced by multidirectional WSS. These findings demonstrate that NP accumulation can be used to differentiate between unstable and stable murine atherosclerosis, but accumulation kinetics are not directly influenced by the WSS condition. This tool could be used as a diagnostic to evaluate the efficacy of experimental therapeutics for atherosclerosis.

Claudin-1-Targeted Nanoparticles for Delivery to Aging-Induced Alterations in the Blood-Brain Barrier.

Aging-induced alterations to the blood-brain barrier (BBB) are increasingly being seen as a primary event in chronic progressive neurological disorders that lead to cognitive decline. With the goal of increasing delivery into the brain in hopes of effectively treating these diseases, a large focus has been placed on developing BBB permeable materials. However, these strategies have suffered from a lack of specificity toward regions of disease progression. Here, we report on the development of a nanoparticle (C1C2-NP) that targets regions of increased claudin-1 expression that reduces BBB integrity. Using dynamic contrast enhanced magnetic resonance imaging, we find that C1C2-NP accumulation and retention is significantly increased in brains from 12 month-old mice as compared to nontargeted NPs and brains from 2 month-old mice. Furthermore, we find C1C2-NP accumulation in brain endothelial cells with high claudin-1 expression, suggesting target-specific binding of the NPs, which was validated through fluorescence imaging, in vitro testing, and biophysical analyses. Our results further suggest a role of claudin-1 in reducing BBB integrity during aging and show altered expression of claudin-1 can be actively targeted with NPs. These findings could help develop strategies for longitudinal monitoring of tight junction protein expression changes during aging as well as be used as a delivery strategy for site-specific delivery of therapeutics at these early stages of disease development.

Multicellular Ovarian Cancer Model for Evaluation of Nanovector Delivery in Ascites and Metastatic Environments.

A novel multicellular model composed of epithelial ovarian cancer and fibroblast cells was developed as an in vitro platform to evaluate nanovector delivery and ultimately aid the development of targeted therapies. We hypothesized that the inclusion of peptide-based scaffold (PuraMatrix) in the spheroid matrix, to represent in vivo tumor microenvironment alterations along with metastatic site conditions, would enhance spheroid cell growth and migration and alter nanovector transport. The model was evaluated by comparing the growth and migration of ovarian cancer cells exposed to stromal cell activation and tissue hypoxia. Fibroblast activation was achieved via the TGF-ß1 mediated pathway and tissue hypoxia via 3D spheroids incubated in hypoxia. Surface-modified nanovector transport was assessed via fluorescence and confocal microscopy. Consistent with previous in vivo observations in ascites and at distal metastases, spheroids exposed to activated stromal microenvironment were denser, more contractile and with more migratory cells than nonactivated counterparts. The hypoxic conditions resulted in negative radial spheroid growth over 5 d compared to a radial increase in normoxia. Nanovector penetration attenuated in PuraMatrix regardless of surface modification due to a denser environment. This platform may serve to evaluate nanovector transport based on ovarian ascites and metastatic environments, and longer term, it provide a means to evaluate nanotherapeutic efficacy.

Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data.

OBJECTIVES: Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. MATERIALS AND METHODS: Samples (n = 36) were evaluated with high-resolution 2DLC-MS/MS and 13C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) in terms of first-line chemotherapy. Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests (RF)) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets. RESULTS: The models predicted patient classifications in the validation subsets with AUC (95 % CI): DC vs. PD (SVM), 0.970(0.961-0.979); CR/PR vs. SD/PD (PLS-DA), 0.880(0.865-0.895); stage I/II/III vs. IV (SVM), 0.902(0.880-0.924). Highest performing model was SVM for DC vs. PD (balanced accuracy = 0.92; kappa = 0.74). CONCLUSION: This study illustrates a comprehensive evaluation of patient tumor-specific metabolic profiles, with the potential to identify disease stage and predict response to first-line chemotherapy.