Impact assessment of metabolite instability in the development and validation of LC-MS/MS bioanalytical assays for measurement of rosuvastatin in human plasma and urine samples.

During bioanalytical assay development and validation, maintaining the stability of the parent drug and metabolites of interest is critical. While stability of the parent drug has been thoroughly investigated, the stability of unanalyzed metabolites is often overlooked. When an unstable metabolite is known or suspected to interfere with measurement of the parent drug or other metabolites of interest through back-conversion or other routes, additional tests with these unstable metabolites should be conducted. Here, the development and validation of two assays for quantification of rosuvastatin, one in human plasma and one in human urine, was reported. To this end, additional sets of quality control samples were added during assay validation to ensure the reliability of the assays. Acid treatment of samples is shown to be necessary for rosuvastatin quantification. In this regard, stability issues caused by the metabolite, rosuvastatin lactone, may have been overlooked if assay development and validation had only considered the parent drug, rosuvastatin. These assays represent a case study for how to develop and validate assays with unstable metabolites. Taken together, unstable metabolites should be included in all applicable stability tests.

Dynamic analysis of naive adaptive brain-machine interfaces.

The closed-loop operation of brain-machine interfaces (BMI) provides a context to discover foundational principles behind human-computer interaction, with emerging clinical applications to stroke, neuromuscular diseases, and trauma. In the canonical BMI, a user controls a prosthetic limb through neural signals that are recorded by electrodes and processed by a decoder into limb movements. In laboratory demonstrations with able-bodied test subjects, parameters of the decoder are commonly tuned using training data that include neural signals and corresponding overt arm movements. In the application of BMI to paralysis or amputation, arm movements are not feasible, and imagined movements create weaker, partially unrelated patterns of neural activity. BMI training must begin naive, without access to these prototypical methods for parameter initialization used in most laboratory BMI demonstrations. Naive adaptive BMI refer to a class of methods recently introduced to address this problem. We first identify the basic elements of existing approaches based on adaptive filtering and define a decoder, ReFIT-PPF to represent these existing approaches. We then present Joint RSE, a novel approach that logically extends prior approaches. Using recently developed human- and synthetic-subjects closed-loop BMI simulation platforms, we show that Joint RSE significantly outperforms ReFIT-PPF and nonadaptive (static) decoders. Control experiments demonstrate the critical role of jointly estimating neural parameters and user intent. In addition, we show that nonzero sensorimotor delay in the user significantly degrades ReFIT-PPF but not Joint RSE, owing to differences in the prior on intended velocity. Paradoxically, substantial differences in the nature of sensory feedback between these methods do not contribute to differences in performance between Joint RSE and ReFIT-PPF. Instead, BMI performance improvement is driven by machine learning, which outpaces rates of human learning in the human-subjects simulation platform. In this regime, nuances of error-related feedback to the human user are less relevant to rapid BMI mastery.

Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry.

The complexity of proteomic instrumentation for LC-MS/MS introduces many possible sources of variability. Data-dependent sampling of peptides constitutes a stochastic element at the heart of discovery proteomics. Although this variation impacts the identification of peptides, proteomic identifications are far from completely random. In this study, we analyzed interlaboratory data sets from the NCI Clinical Proteomic Technology Assessment for Cancer to examine repeatability and reproducibility in peptide and protein identifications. Included data spanned 144 LC-MS/MS experiments on four Thermo LTQ and four Orbitrap instruments. Samples included yeast lysate, the NCI-20 defined dynamic range protein mix, and the Sigma UPS 1 defined equimolar protein mix. Some of our findings reinforced conventional wisdom, such as repeatability and reproducibility being higher for proteins than for peptides. Most lessons from the data, however, were more subtle. Orbitraps proved capable of higher repeatability and reproducibility, but aberrant performance occasionally erased these gains. Even the simplest protein digestions yielded more peptide ions than LC-MS/MS could identify during a single experiment. We observed that peptide lists from pairs of technical replicates overlapped by 35-60%, giving a range for peptide-level repeatability in these experiments. Sample complexity did not appear to affect peptide identification repeatability, even as numbers of identified spectra changed by an order of magnitude. Statistical analysis of protein spectral counts revealed greater stability across technical replicates for Orbitraps, making them superior to LTQ instruments for biomarker candidate discovery. The most repeatable peptides were those corresponding to conventional tryptic cleavage sites, those that produced intense MS signals, and those that resulted from proteins generating many distinct peptides. Reproducibility among different instruments of the same type lagged behind repeatability of technical replicates on a single instrument by several percent. These findings reinforce the importance of evaluating repeatability as a fundamental characteristic of analytical technologies.

Proteomics-based approach to elucidate the mechanism of antitumor effect of curcumin in cervical cancer.

Cervical cancer is the second leading cause of cancer death for women in the world. A potential target for preventing and treating cervical cancer is cyclooxygenase-2 (cox-2). Curcumin is an anti-inflammatory agent that is known to have anti-cox-2 activity. In this study we examined the expression of cox-2 in cervical cancer and its precursors by immunohistochemistry. The effect of curcumin in inhibiting cervical cancer cells was determined via 2-dimensional gel electrophoresis, data analysis, and ingenuity pathway analysis. No significant differences in the expression of cox-2 in squamous cell carcinoma, and carcinoma in situ were observed. However, there was a statistically significant difference in the expression of cox-2 in adenocarcinoma in comparison to normal (p value=0.01) and squamous cell carcinoma (p value=0.02) tissues. Proteins associated with cancer and cell cycle were significantly altered in cultured cells. Curcumin may have antitumor effect in cervical cancer.

Smoothness as a failure mode of Bayesian mixture models in brain-machine interfaces.

Various recursive Bayesian filters based on reach state equations (RSE) have been proposed to convert neural signals into reaching movements in brain-machine interfaces. When the target is known, RSE produce exquisitely smooth trajectories relative to the random walk prior in the basic Kalman filter. More realistically, the target is unknown, and gaze analysis or other side information is expected to provide a discrete set of potential targets. In anticipation of this scenario, various groups have implemented RSE-based mixture (hybrid) models, which define a discrete random variable to represent target identity. While principled, this approach sacrifices the smoothness of RSE with known targets. This paper combines empirical spiking data from primary motor cortex and mathematical analysis to explain this loss in performance. We focus on angular velocity as a meaningful and convenient measure of smoothness. Our results demonstrate that angular velocity in the trajectory is approximately proportional to change in target probability. The constant of proportionality equals the difference in heading between parallel filters from the two most probable targets, suggesting a smoothness benefit to more narrowly spaced targets. Simulation confirms that measures to smooth the data likelihood also improve the smoothness of hybrid trajectories, including increased ensemble size and uniformity in preferred directions. We speculate that closed-loop training or neuronal subset selection could be used to shape the user's tuning curves towards this end.

Liver proteome response of largemouth bass (Micropterus salmoides) exposed to several environmental contaminants: potential insights into biomarker development.

Liver proteome response of largemouth bass (Micropterus salmoides) exposed to environmental contaminants was analyzed to identify novel biomarkers of exposure. Adult male bass were exposed to cadmium chloride (CdCl(2)), atrazine, PCB 126, phenanthrene, or toxaphene via intraperitoneal injection with target body burdens of 0.00067, 3.0, 2.5, 50, and 100 microg/g, respectively. After a 96 h exposure, hepatic proteins were separated with two-dimensional gel electrophoresis and differentially expressed proteins (vs. controls) recognized and identified with MALDI-TOF/TOF mass spectrometry. We identified, 30, 18, eight, 19, and five proteins as differentially expressed within the CdCl(2), atrazine, PCB 126, phenanthrene, and toxaphene treatments, respectively. Alterations were observed in the expression of proteins associated with cellular ion homeostasis (toxaphene), oxidative stress (phenanthrene, PCB 126), and energy production including glycolysis (CdCl(2), atrazine) and ATP synthesis (atrazine). This work supports the further evaluation of several of these proteins as biomarkers of contaminant exposure in fish.