Pharmacokinetics of transdermal buprenorphine patch in the elderly.

PURPOSE: Transdermal buprenorphine patches provide comparable pain relief to that of low-potency opioids in elderly individuals. However, specific data on their use in elderly individuals is limited. This study investigated and compared the PK of buprenorphine transdermal patches in elderly (≥ 75 years) versus younger (50-60 years) individuals. METHODS: This was a multiple-dose, open-label, parallel-group study in healthy volunteers split into two age groups (younger, 50-60 years; elderly, ≥ 75 years) with 37 individuals in each. Study participants received two consecutive 7-day buprenorphine 5 µg/h transdermal patch applications, and blood samples were collected on the week of the second patch application [day 7 (predose), days 8, 9, 10, 12, and 14] to determine PK at steady state. Pharmacokinetic parameters were determined for buprenorphine and norbuprenorphine. Safety was assessed by analyzing adverse events, hematology, clinical chemistry, urine analysis, vital signs, electrocardiogram (ECG), and physical examinations. RESULTS: The area under the plasma concentration-time curve at steady state (AUC(tau)), measured over one dosing interval, was similar for elderly [mean ± standard deviation (SD) 9,940 pg/h/ml (4,827 pg/h/ml] and younger [mean ± SD 11,309 (3,670 pg/h/ml] individuals. Bioequivalence was not demonstrated between groups, which may be attributable to the relatively high level of variability in individual plasma profiles. More adverse events were reported by younger (216) than elderly (164) study participants. CONCLUSIONS: No dosage alterations are necessary for PK reasons when treating elderly people with buprenorphine transdermal patches.

Enhancement of plant metabolite fingerprinting by machine learning.

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Towards quantitatively reproducible substrates for SERS.

There is a need for a method to facilitate the development of novel, reproducible colloidal surface-enhanced Raman scattering (SERS) substrates to encourage the use of SERS in applied studies. In this study we show for the first time that by using suitably designed SERS experiments in conjunction with multivariate analysis of variance (MANOVA), an objective assessment of colloidal SERS reproducibility can be made. This is demonstrated with the analyte cresyl violet, but could be extended to any analyte of interest for which reproducible SERS data are needed.

Measuring the metabolome: current analytical technologies.

The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.

High-throughput metabolic fingerprinting of legume silage fermentations via Fourier transform infrared spectroscopy and chemometrics.

Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm(-1)) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.

Metabolic fingerprinting of salt-stressed tomatoes.

The aim of this study was to adopt the approach of metabolic fingerprinting through the use of Fourier transform infrared (FT-IR) spectroscopy and chemometrics to study the effect of salinity on tomato fruit. Two varieties of tomato were studied, Edkawy and Simge F1. Salinity treatment significantly reduced the relative growth rate of Simge F1 but had no significant effect on that of Edkawy. In both tomato varieties salt-treatment significantly reduced mean fruit fresh weight and size class but had no significant affect on total fruit number. Marketable yield was however reduced in both varieties due to the occurrence of blossom end rot in response to salinity. Whole fruit flesh extracts from control and salt-grown tomatoes were analysed using FT-IR spectroscopy. Each sample spectrum contained 882 variables, absorbance values at different wavenumbers, making visual analysis difficult and therefore machine learning methods were applied. The unsupervised clustering method, principal component analysis (PCA) showed no discrimination between the control and salt-treated fruit for either variety. The supervised method, discriminant function analysis (DFA) was able to classify control and salt-treated fruit in both varieties. Genetic algorithms (GA) were applied to identify discriminatory regions within the FT-IR spectra important for fruit classification. The GA models were able to classify control and salt-treated fruit with a typical error, when classifying the whole data set, of 9% in Edkawy and 5% in Simge F1. Key regions were identified within the spectra corresponding to nitrile containing compounds and amino radicals. The application of GA enabled the identification of functional groups of potential importance in relation to the response of tomato to salinity.