Simultaneous Determination of N,N-Diethyl-M-Toluamide and Permethrin by GC-MS and Pyridostigmine Bromide by HPLC in Human Plasma. Application to Pharmacokinetic Studies

Determination of Pyridostigmine Bromide in Presence of its Related Impurities by Four Modified Classical Least Square Based Models: A Comparative Study

Introduction:

Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study.

Methods:

3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed.

Results:

The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed.

Conclusion:

Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.

Orthogonal projection to latent structures and first derivative for manipulation of PLSR and SVR chemometric models' prediction: A case study

Novel manipulations of the well-established multivariate calibration models namely; partial least square regression (PLSR) and support vector regression (SVR) are introduced in the presented comparative study. Two preprocessing methods comprising first derivatization and orthogonal projection to latent structures (OPLS) are implemented prior to modeling with PLSR and SVR. Quantitative determination of pyridostigmine bromide (PR) in existence of its two associated substances; impurity a (IMP A) and impurity b (IMP B); was utilized as a case study for achieving comparison. A series consisting of 16 mixtures with numerous percentages of the studied compounds was applied for implementation of a 3 factor 4 level experimental design. Additionally, a series consisting of 9 mixtures was employed in an independent test to verify the predictive power of the suggested models. Significant improvement of predictive abilities of the two studied chemometric models was attained via implementation of OPLS processing method. The root mean square error of prediction RMSEP for the test set mixtures was employed as a key comparison tool. About PLSR model, RMSEP was found 0.5283 without preprocessing method, 1.1750 when first derivative data was used and 0.2890 when OPLS preprocessing method was applied. With regard to SVR model, RMSEP was found 0.2173 without preprocessing method, 0.3516 when first derivative data was used and 0.1819 when OPLS preprocessing method was applied.

On-line solid phase extraction-high performance liquid chromatography-isotope dilution-tandem mass spectrometry approach to quantify N,N-diethyl-m-toluamide and oxidative metabolites in urine

Human exposure to N,N-diethyl-m-toluamide (DEET) occurs because of the widespread use of DEET as an active ingredient in insect repellents. However, information on the extent of such exposure is rather limited. Therefore, we developed a fast on-line solid phase extraction-high performance liquid chromatography-isotope dilution tandem mass spectrometry (HPLC-MS/MS) method to measure in urine the concentrations of DEET and two of its oxidative metabolites: N,N-diethyl-3-(hydroxymethyl)benzamide and 3-(diethylcarbamoyl)benzoic acid (DCBA). To the best of our knowledge, this is the first HPLC-MS/MS method for the simultaneous quantification of DEET and its select metabolites in human urine. After enzymatic hydrolysis of the conjugated species in 0.1 mL of urine, the target analytes were retained and pre-concentrated on a monolithic column, separated from each other and from other urinary biomolecules on a reversed-phase analytical column, and detected by atmospheric pressure chemical ionization in positive ion mode. The limits of detection ranged from 0.1 ng mL(-1) to 1.0 ng mL(-1), depending on the analyte. Accuracy ranged between 90.4 and 104.9%, and precision ranged between 5.5 and 13.1% RSD, depending on the analyte and the concentration. We tested the usefulness of this method by analyzing 75 urine samples collected anonymously in the Southeastern United States in June 2012 from adults with no known exposure to DEET. Thirty eight samples (51%) tested positive for at least one of the analytes. We detected DCBA most frequently and at the highest concentrations. Our results suggest that this method can be used for the analysis of a large number of samples for epidemiological studies to assess human exposure to DEET.