Dried blood spot liquid chromatography assay for therapeutic drug monitoring of metformin

The use of blood spot collection cards is a simple way to obtain specimens for analysis of drugs for the purpose of therapeutic drug monitoring, assessing adherence to medications and preventing toxicity in routine clinical setting. We describe the development and validation of a microanalytical technique for the determination of metformin from dried blood spots. The method is based on reversed phase high-performance liquid chromatography with ultraviolet detection. Drug recovery in the developed method was found to be more than 84%. The limits of detection and quantification were calculated to be to be 90 and 150 ng/ml, respectively. The intraday and interday precision (measured by CV%) was always less than 9%. The accuracy (measured by relative error, %) was always less than 12%. Stability analysis showed that metformin is stable for at least 2 months when stored at -70 degrees C. The small volume of blood required (10 mu L), combined with the simplicity of the analytical technique makes this a useful procedure for monitoring metformin concentrations in routine clinical settings. The method is currently being applied to the analysis of blood spots taken from diabetic patients to assess adherence to medications and relationship between metformin level and metabolic control of diabetes. (c) 2006 Elsevier B.V. All rights reserved.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Analysis of Extended Partial Least Squares for Monitoring Large-Scale Processes

This paper theoretically analysis the recently proposed "Extended Partial Least Squares" (EPLS) algorithm. After pointing out some conceptual deficiencies, a revised algorithm is introduced that covers the middle ground between Partial Least Squares and Principal Component Analysis. It maximises a covariance criterion between a cause and an effect variable set (partial least squares) and allows a complete reconstruction of the recorded data (principal component analysis). The new and conceptually simpler EPLS algorithm has successfully been applied in detecting and diagnosing various fault conditions, where the original EPLS algorithm did only offer fault detection.

Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.