Metabolomic response to coffee consumption: application to a three-stage clinical trial.

BACKGROUND: Coffee is widely consumed and contains many bioactive compounds, any of which may impact pathways related to disease development. OBJECTIVE: To identify individual metabolite changes in response to coffee. METHODS: We profiled the metabolome of fasting serum samples collected from a previously reported single-blinded, three-stage clinical trial. Forty-seven habitual coffee consumers refrained from drinking coffee for 1 month, consumed four cups of coffee/day in the second month and eight cups/day in the third month. Samples collected after each coffee stage were subject to nontargeted metabolomic profiling using UPLC-ESI-MS/MS. A total of 733 metabolites were included for univariate and multivariate analyses. RESULTS: A total of 115 metabolites were significantly associated with coffee intake (P < 0.05 and Q < 0.05). Eighty-two were of known identity and mapped to one of 33 predefined biological pathways. We observed a significant enrichment of metabolite members of five pathways (P < 0.05): (i) xanthine metabolism: includes caffeine metabolites, (ii) benzoate metabolism: reflects polyphenol metabolite products of gut microbiota metabolism, (iii) steroid: novel but may reflect phytosterol content of coffee, (iv) fatty acid metabolism (acylcholine): novel link to coffee and (v) endocannabinoid: novel link to coffee. CONCLUSIONS: The novel metabolites and candidate pathways we have identified may provide new insight into the mechanisms by which coffee may be exerting its health effects.

On the increase of predictive performance with high-level data fusion.

The combination of the different data sources for classification purposes, also called data fusion, can be done at different levels: low-level, i.e. concatenating data matrices, medium-level, i.e. concatenating data matrices after feature selection and high-level, i.e. combining model outputs. In this paper the predictive performance of high-level data fusion is investigated. Partial least squares is used on each of the data sets and dummy variables representing the classes are used as response variables. Based on the estimated responses y(j) for data set j and class k, a Gaussian distribution p(g(k)|y(j)) is fitted. A simulation study is performed that shows the theoretical performance of high-level data fusion for two classes and two data sets. Within group correlations of the predicted responses of the two models and differences between the predictive ability of each of the separate models and the fused models are studied. Results show that the error rate is always less than or equal to the best performing subset and can theoretically approach zero. Negative within group correlations always improve the predictive performance. However, if the data sets have a joint basis, as with metabolomics data, this is not likely to happen. For equally performing individual classifiers the best results are expected for small within group correlations. Fusion of a non-predictive classifier with a classifier that exhibits discriminative ability lead to increased predictive performance if the within group correlations are strong. An example with real life data shows the applicability of the simulation results.

Dynamic metabolomic data analysis: a tutorial review.

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a 'dynamic' method. Some of the methods are illustrated with real-life metabolomics examples.

Examples of NIR based real time release in tablet manufacturing.

Real time release (RTR) of products is a new paradigm in the pharmaceutical industry. An RTR system assures that when the last manufacturing step is passed all the final release criteria are met. Various types of models can be used within the RTR framework. For each RTR system, the monitoring capability, control capability and RTR capability need to be tested. This paper presents some practical examples within the RTR framework using near-infrared and process data obtained from a tablet manufacturing process.

Simple assessment of homogeneity in pharmaceutical mixing processes using a near-infrared reflectance probe and control charts.

Determination of homogeneous mixing of the active pharmaceutical ingredient (API) is an important in-process control within the manufacturing of solid dosage forms. In this paper two new near-infrared (NIR) based methods were presented; a qualitative and a quantitative method. Both methods are based on the calculation of net analyte signal (NAS) models which were very easy to develop, specific with respect to the API and required no additional reference analysis. Using a well-mixed batch as a 'golden standard' batch, control charts were developed and used for monitoring the homogeneity of other batches with NIR. The methods were fast, easy to use, non-destructive and provided statistical tests of homogeneity. A mixing study was characterized with the two methods and the methods were validated by comparison with traditional HPLC analysis.

Net analyte signal based statistical quality control.

Net analyte signal statistical quality control (NAS-SQC) is a new methodology to perform multivariate product quality monitoring based on the net analyte signal approach. The main advantage of NAS-SQC is that the systematic variation in the product due to the analyte (or property) of interest is separated from the remaining systematic variation due to all other compounds in the matrix. This enhances the ability to flag products out of statistical control. Using control charts, the analyte content, variation of other compounds, and residual variation can be monitored. As an example, NAS-SQC is used to appreciate the control content uniformity of a commercially available pharmaceutical tablet product measured with near-infrared spectroscopy. Using the NAS chart, the active pharmaceutical ingredient (API) content is easily monitored for new tablets. However, since quality is a multivariate property, other quality parameters of the tablets are also monitored simultaneously. It will be demonstrated that, besides the API content, the water content of the tablets as well as the homogeneity of the other compounds is monitored.

In-line reaction monitoring of a methyl methacrylate and N, N-dimethylacrylamide copolymerization reaction using near-infrared spectroscopy.

Fast and accurate monitoring of monomer concentration during copolymerization reactions is of much interest. It is known that near-infrared spectroscopy (NIRS) can be used to monitor polymerization reactions. Here, a free radical solution copolymerization reaction between methyl methacrylate and N,N-dimethylacrylamide is considered. NIR spectra were measured in-line with a transflectance probe. The spectra of both involved monomers are very similar, making monitoring with NIRS challenging. It is shown that the NIRS calibration can be set up with only a few (5) off-line measured mixtures. Several validation methods for such a NIRS calibration model are discussed and tested. NIRS is used to follow conversion of the two monomers in a copolymerization reaction on-line.

New indicator for optimal preprocessing and wavelength selection of near-infrared spectra.

Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing techniques and selecting different wavelengths is to compare prediction statistics computed with an independent set of data not used to make the actual calibration model. When the errors of reference value are large, no such values are available at all, or only a limited number of samples are available, other methods exist to evaluate the preprocessing method and wavelength selection. In this work we present a new indicator (SE) that only requires blank sample spectra, i.e., spectra of samples that are mixtures of the interfering constituents (everything except the analyte), a pure analyte spectrum, or alternatively, a sample spectrum where the analyte is present. The indicator is based on computing the net analyte signal of the analyte and the total error, i.e., instrumental noise and bias. By comparing the indicator values when different preprocessing techniques and wavelength selections are applied to the spectra, the optimal preprocessing technique and the optimal wavelength selection can be determined without knowledge of reference values, i.e., it minimizes the non-related spectral variation. The SE indicator is compared to two other indicators that also use net analyte signal computations. To demonstrate the feasibility of the SE indicator, two near-infrared spectral data sets from the pharmaceutical industry were used, i.e., diffuse reflectance spectra of powder samples and transmission spectra of tablets. Especially in pharmaceutical spectroscopic applications, it is expected beforehand that the non-related spectral variation is rather large and it is important to remove it. The indicator gave excellent results with respect to wavelength selection and optimal preprocessing. The SE indicator performs better than the two other indicators, and it is also applicable to other situations where the Beer-Lambert law is valid.

Multiblock PLS analysis of an industrial pharmaceutical process.

The performance of an industrial pharmaceutical process (production of an active pharmaceutical ingredient by fermentation, API) was modeled by multiblock partial least squares (MBPLS). The most important process stages are inoculum production and API production fermentation. Thirty batches (runs) were produced according to an experimental planning. Rather than merging all these data into a single block of independent variables (as in ordinary PLS), four data blocks were used separately (manipulated and quality variables for each process stage). With the multiblock approach it was possible to calculate weights and scores for each independent block. It was found that the inoculum quality variables were highly correlated with API production for nominal fermentations. For the nonnominal fermentations, the manipulations of the fermentation stage explained the amount of API obtained (especially the pH and biomass concentration). Based on the above process analysis it was possible to select a smaller set of variables with which a new model was built. The amount of variance predicted of the final API concentration (cross-validation) for this model was 82.4%. The advantage of the multiblock model over the standard PLS model is that the contributions of the two main process stages to the API volumetric productivity were determined.