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The characteristics of automated on-line solid phase extraction with liquid chromatography-mass spectrometry (SPE-LC-MS) are very amenable for flexibility and throughput in therapeutic drug monitoring (TDM). We demonstrate this concept of automated, on-line SPE-LC-MS for the analysis of clozapine and metabolites (desmethylclozapine and clozapine-N-oxide) in serum. Method development, optimisation and validation are described and a comparison with previously published methods for the determination of clozapine and metabolites in serum and plasma is made. Optimisation of chromatographic and SPE conditions for increased throughput resulted in SPE-LC-MS cycle times of only about 2.2 min, demonstrating the great potential of automated on-line SPE-LC-MS for TDM. The new method is shown to be clearly favourable, in particular in terms of ease of sample handling, throughput and detection limits. Recovery is essentially quantitative. Detection limits are at about 0.15-0.3 ng ml(-1), depending on the ionisation source used. Calibration follows a quadratic model for clozapine and its N-oxide and a linear model for the desmethyl metabolite (all cases: R > 0.99). Accuracy, evaluated at three concentration levels spanning the whole therapeutic range, shows that bias is less than 10%. Precision (intra - and inter assay) ranges from about 5% R.S.D. at the high end of the therapeutic range (700-1,000 ng ml(-1)) to about 20% R.S.D. (OECD defined limit) at the lower limit of quantitation ( approximately 50 ng ml(-1)). The lower limit of quantitation is well below the low end of the therapeutic range at 350 ng ml(-1).

In this paper, augmentation has been applied to data matrices, which originate from hyphenated methods that share the same mode of detection, but use different separation methods, HPLC-DAD and MEKC-DAD. A novel method, wavelength shift eigenstructure tracking (WET), has been proposed for the alignment between the wavelength scale of both detectors. WET proves to be suitable for the detection as well as correction of wavelength shift between both detectors. After correction of the wavelength scale, data obtained on both systems have been augmented and submitted to iterative target transformation factor analysis. Augmented curve resolution provides significantly better estimates of the chromatographic and electrophoretic profiles and spectra than the use of non-augmented curve resolution on HPLC and MEKC data separately. It is particularly useful when the pure fraction of a chromatographic peak is less than 0.10. Finally, the relative weight of MEKC versus HPLC in augmentation may be increased using intensity and noise normalisation. However, since noise normalisation and its accompanying decrease in signal-to-noise ratio leads to a loss of information, and, since intensity normalisation may cause a failure of the augmented curve resolution algorithm, benefits and drawbacks of normalisation should be weighed on a case-by-case basis.

The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with (non-)linear regression methods. The number of hidden nodes is optimized by a lateral inhibition method. Overfitting is controlled by cross-validation using the leave one out method (LOOM). Data sets of linear and non-linear response surfaces (capacity factors) were taken from literature. The results show that neural networks offer promising possibilities in HPLC method development. The predictive results were better or comparable to those obtained with linear and non-linear regression models.

Infrared (IR) spectroscopy is used to analyze urinary calculus (renal stone) constituents. However, interpretation of IR spectra for quantifying urinary calculus constituents in mixtures is difficult, requiring expert knowledge by trained technicians. In our laboratory IR spectra of unknown calculi are compared with references spectra in a computerized library search of 235 reference spectra from various mixtures of constituents in different proportions, followed by visual interpretation of band intensities for more precise semiquantitative determination of the composition. To minimize the need for this last step, we tested artificial neural network models for detecting the most frequently occurring compositions of urinary calculi. Using constrained mixture designs, we prepared various samples containing ammonium hydrogen urate, brushite, carbonate apatite, cystine, struvite, uric acid, weddellite, and whewellite for use as a training set. We assayed known artificial mixtures as well as selected patients' samples from which the semiquantitative compositions were determined by computerized library search followed by visual interpretation. Neural network analysis was more accurate than the library search and required less expert knowledge because careful visual inspection of the band intensities could be omitted. We conclude that neural networks are promising tools for routine quantification of urinary calculus compositions and for other related types of analyses in the clinical laboratory.

As a contribution to the structure-activity relationship of silver sulfanilamide complexes, the pKa-values of thirteen sulfanilamides and the log K-values of their related silver compounds were determined using a microcomputer-controlled titrator which determines the silver ion concentration and hydrogen ion concentration in a combined measurement. Predictions on antibacterial effectiveness and the risk of sensitization reactions of some of the investigated silver sulfanilamides are made on the basis of the conditional stability constants computed from the pKa- and log K-values at pH = 7.4.

A two-phase potentiometric titration procedure of barbiturates with mercury(II) was developed. The composition of the formed mercury-barbiturate complexes was elucidated by infrared spectrometry. In the new titration procedure the barbituric acid derivative is dissolved in an aqueous borate buffer. An organic phase consisting of chloroform and benzyl alcohol is added, and the vigorously stirred contents of the titration vessel are titrated by a mercury(II) nitrate solution. In the potentiometric determination of the end-point a rotating mercury electrode is used as an indicator electrode that also serves as an efficient stirrer. The two-phase procedure was compared with a one-phase mercurimetric potentiometric titration in borate buffer and with the potentiometric titration by sodium hydroxide in an ethanol-water solution. The proposed two-phase procedure is superior to both methods because lower concentrations of barbiturates (10(-3) - 10(-4) M) can be determined successfully. The one-phase procedure suffers from systematic errors, while the titration with sodium hydroxide is less precise at concentration levels of the barbiturates prevailing in content uniformity studies. By the two-phase procedure the direct titration of phenobarbital and mephobarbital in a dry mix of tablet excipients was possible with a relative standard deviation smaller than 1.5 percent.

A two-phase potentiometric titration procedure for barbiturates with mercury(II) was developed. In the new titration procedure the barbituric acid derivative is dissolved in an aqueous borate buffer. An organic phase consisting of chloroform and benzyl alcohol is added, and the vigourously stirred contents of the titration vessel are titrated by a mercury(II) nitrate solution. The theory of the conditional formation constant in a two-phase system is derived to provide a basis for the selection of optimal titration conditions. It is shown that the new conditional formation constant of the two-phase system, K, increases if the mercury-barbiturate complex is extracted into the organic layer. By extraction of the barbituric acid derivative into the organic layer K is diminished, but this effect can be minimized by a correct choice of the pH of the aqueous layer. The influence of these factors on the pM jump in the equivalence point is demonstrated by calculation of the corresponding theoretical titration curves.