Effect of Composition and Size on Surface Properties of Anti-Cancer Nanoparticles.

Liposomal formulations offer significant advantages as anticancer drug carriers for targeted drug delivery; however, due to their complexity, clinical translation has been challenging. In addition, liposomal product manufacturing has been interrupted in the past, as was the case for Doxil® (doxorubicin hydrochloride liposome injection). Here, interfacial tension (IFT) measurements were investigated as a potential physicochemical characterization tool to aid in liposomal product characterization during development and manufacturing. A pendant drop method using an optical tensiometer was used to measure the interfacial tension of various analogues of Doxil® liposomal suspensions in air and in dodecane. The effect of liposome concentration, formulation (PEG and cholesterol content), presence of encapsulated drug, as well as average particle size was analyzed. It was observed that Doxil® analog liposomes demonstrate surfactant-like behavior with a sigmoidal-shape interfacial tension vs. concentration curve. This behavior was heavily dependent on PEG content, with a complete loss of surfactant-like behavior when PEG was removed from the formulation. In addition to interfacial tension, three data analyses were identified as able to distinguish between formulations with variations in PEG, cholesterol, and particle size: (i) polar and non-polar contribution to interfacial tension, (ii) liposomal concentration at which the polar and non-polar components were equal, and (iii) rate of interfacial tension decay after droplet formation, which is indicative of how quickly liposomes migrate from the bulk of the solution to the surface. We demonstrate for the first time that interfacial tension can be used to detect certain liposomal formulation changes, such as PEG content, encapsulated drug presence, and size variability, and may make a useful addition to physicochemical characterization during development and manufacturing of liposomal products.

Early detection of metabolic dysregulation using water T2 analysis of biobanked samples.

BACKGROUND: The ability to use frozen biobanked samples from cohort studies and clinical trials is critically important for biomarker discovery and validation. Here we investigated whether plasma and serum water transverse relaxation times (T2) from frozen biobanked samples could be used as biomarkers for metabolic syndrome (MetS) and its underlying conditions, specifically insulin resistance, dyslipidemia, and subclinical inflammation. METHODS: Plasma and serum aliquots from 44 asymptomatic, non-diabetic human subjects were biobanked at -80°C for 7-9 months. Water T2 measurements were recorded at 37°C on 50 µL of unmodified plasma or serum using benchtop nuclear magnetic resonance relaxometry. The T2 values for freshly drawn and once-frozen-thawed ("frozen") samples were compared using Huber M-values (M), Lin concordance correlation coefficients (ρc), and Bland-Altman plots. Water T2 values from frozen plasma and serum samples were compared with >130 metabolic biomarkers and analyzed using multi-variable linear/logistic regression and ROC curves. RESULTS: Frozen plasma water T2 values were highly correlated with fresh (M=0.94, 95% CI 0.89, 0.97) but showed a lower level of agreement (ρc=0.74, 95% CI 0.62, 0.82) because of an average offset of -5.6% (-7.1% for serum). Despite the offset, frozen plasma water T2 was strongly correlated with markers of hyperinsulinemia, dyslipidemia, and inflammation and detected these conditions with 89% sensitivity and 91% specificity (100%/63% for serum). Using optimized cut points, frozen plasma and serum water T2 detected hyperinsulinemia, dyslipidemia, and inflammation in 23 of 44 subjects, including nine with an early stage of metabolic dysregulation that did not meet the clinical thresholds for prediabetes or MetS. CONCLUSION: Plasma and serum water T2 values from once-frozen-thawed biobanked samples detect metabolic dysregulation with high sensitivity and specificity. However, the cut points for frozen biobanked samples must be calibrated independent of those for freshly drawn plasma and serum.

Aptamer-based search for correlates of plasma and serum water T2: implications for early metabolic dysregulation and metabolic syndrome.

BACKGROUND: Metabolic syndrome is a cluster of abnormalities that increases the risk for type 2 diabetes and atherosclerosis. Plasma and serum water T2 from benchtop nuclear magnetic resonance relaxometry are early, global and practical biomarkers for metabolic syndrome and its underlying abnormalities. In a prior study, water T2 was analyzed against ~ 130 strategically selected proteins and metabolites to identify associations with insulin resistance, inflammation and dyslipidemia. In the current study, the analysis was broadened ten-fold using a modified aptamer (SOMAmer) library, enabling an unbiased search for new proteins correlated with water T2 and thus, metabolic health. METHODS: Water T2 measurements were recorded using fasting plasma and serum from non-diabetic human subjects. In parallel, plasma samples were analyzed using a SOMAscan assay that employed modified DNA aptamers to determine the relative concentrations of 1310 proteins. A multi-step statistical analysis was performed to identify the biomarkers most predictive of water T2. The steps included Spearman rank correlation, followed by principal components analysis with variable clustering, random forests for biomarker selection, and regression trees for biomarker ranking. RESULTS: The multi-step analysis unveiled five new proteins most predictive of water T2: hepatocyte growth factor, receptor tyrosine kinase FLT3, bone sialoprotein 2, glucokinase regulatory protein and endothelial cell-specific molecule 1. Three of the five strongest predictors of water T2 have been previously implicated in cardiometabolic diseases. Hepatocyte growth factor has been associated with incident type 2 diabetes, and endothelial cell specific molecule 1, with atherosclerosis in subjects with diabetes. Glucokinase regulatory protein plays a critical role in hepatic glucose uptake and metabolism and is a drug target for type 2 diabetes. By contrast, receptor tyrosine kinase FLT3 and bone sialoprotein 2 have not been previously associated with metabolic conditions. In addition to the five most predictive biomarkers, the analysis unveiled other strong correlates of water T2 that would not have been identified in a hypothesis-driven biomarker search. CONCLUSIONS: The identification of new proteins associated with water T2 demonstrates the value of this approach to biomarker discovery. It provides new insights into the metabolic significance of water T2 and the pathophysiology of metabolic syndrome.

Water T2 as an early, global and practical biomarker for metabolic syndrome: an observational cross-sectional study.

BACKGROUND: Metabolic syndrome (MetS) is a highly prevalent condition that identifies individuals at risk for type 2 diabetes mellitus and atherosclerotic cardiovascular disease. Prevention of these diseases relies on early detection and intervention in order to preserve pancreatic ß-cells and arterial wall integrity. Yet, the clinical criteria for MetS are insensitive to the early-stage insulin resistance, inflammation, cholesterol and clotting factor abnormalities that characterize the progression toward type 2 diabetes and atherosclerosis. Here we report the discovery and initial characterization of an atypical new biomarker that detects these early conditions with just one measurement. METHODS: Water T2, measured in a few minutes using benchtop nuclear magnetic resonance relaxometry, is exquisitely sensitive to metabolic shifts in the blood proteome. In an observational cross-sectional study of 72 non-diabetic human subjects, the association of plasma and serum water T2 values with over 130 blood biomarkers was analyzed using bivariate, multivariate and logistic regression. RESULTS: Plasma and serum water T2 exhibited strong bivariate correlations with markers of insulin, lipids, inflammation, coagulation and electrolyte balance. After correcting for confounders, low water T2 values were independently and additively associated with fasting hyperinsulinemia, dyslipidemia and subclinical inflammation. Plasma water T2 exhibited 100% sensitivity and 87% specificity for detecting early insulin resistance in normoglycemic subjects, as defined by the McAuley Index. Sixteen normoglycemic subjects with early metabolic abnormalities (22% of the study population) were identified by low water T2 values. Thirteen of the 16 did not meet the harmonized clinical criteria for metabolic syndrome and would have been missed by conventional screening for diabetes risk. Low water T2 values were associated with increases in the mean concentrations of 6 of the 16 most abundant acute phase proteins and lipoproteins in plasma. CONCLUSIONS: Water T2 detects a constellation of early abnormalities associated with metabolic syndrome, providing a global view of an individual's metabolic health. It circumvents the pitfalls associated with fasting glucose and hemoglobin A1c and the limitations of the current clinical criteria for metabolic syndrome. Water T2 shows promise as an early, global and practical screening tool for the identification of individuals at risk for diabetes and atherosclerosis.