Comparison of the quantitative performances and measurement uncertainty estimates obtained during method validation versus routine applications of a novel hydrophilic interaction chromatography method for the determination of cidofovir in human plasma

Development, validation and different approaches for the measurement uncertainty of a multi-class veterinary drugs residues LC-MS method for feeds

A sensitive method for the simultaneous residues analysis of 62 veterinary drugs in feeds by liquid chromatography-tandem mass spectrometry has been developed and validated in accordance to Commission Decision 657/2002/EC. Additionally, limits of detection (LOD), limits of quantitation (LOQ), matrix effects and measurement uncertainty were also assessed. Extraction was performed for all analytes and respective internal standards in a single step and chromatographic separation was achieved in only 12min. LOQ were set to 0.63-5.00μgkg^-1 (amphenicols), 0.63-30.00μgkg^-1 (avermectins), 0.63μgkg^-1 (benzimidazoles), 0.25-200.00μgkg^-1 (coccidiostats), 0.63-200.00μgkg^-1 (lincosamides and macrolides), 0.25-5.00μgkg^-1 (nitrofurans), 0.63-20.00μgkg^-1 (fluoroquinolones and quinolones), 15.00μgkg^-1 (quinoxaline), 0.63-7.50μgkg^-1 (sulfonamides), 0.63-20.00μgkg^-1 (tetracyclines), 0.25μgkg^-1 (β-agonists), and 30.00μgkg^-1 (β-lactams). The top-down approach was adequate for the calculation of measurement uncertainty for all analytes, except the banned substances, which should be rather assessed by the bottom-up approach. Routine analysis of different types of feeds was then carried out. An interesting profile of residues of veterinary drugs among samples was revealed, enlightening the need for stricter control in producing animals. Among the total of 27 feed samples, 20 analytes could be detected/quantified, ranging from trace levels to very high concentrations. A high throughput screening/confirmatory method for the residue analysis of several veterinary drugs in feeds was proposed as a helpful control tool.

Recent advances in hydrophilic interaction chromatography for quantitative analysis of endogenous and pharmaceutical compounds in plasma samples

There is an increasing need for new analytical methods that can handle a large number of analytes in complex matrices. Hydrophilic interaction chromatography (HILIC) has recently been demonstrated as an important supplement to reversed-phase liquid chromatography for polar analytes, particularly endogenous compounds. With the increasing popularity of HILIC, progressively more polar phases with diverse functional groups have been developed. In addition, the coupling of HILIC to mass spectrometry offers the advantages of improved sensitivity by employing an organic-rich mobile phase. This article reviews recent applications of HILIC for the analysis of endogenous and pharmaceutical compounds in plasma samples. Furthermore, based on recent studies, we provide a discussion of column selection, sample pretreatment for HILIC analysis, and detection sensitivity.

Fast method for the simultaneous quantification of toxic polyphenols applied to the selection of genotypes of yam bean (Pachyrhizus sp.) seeds

The purpose of the research was to develop and validate a rapid quantification method able to screen many samples of yam bean seeds to determine the content of two toxic polyphenols, namely pachyrrhizine and rotenone. The analytical procedure described is based on the use of an internal standard (dihydrorotenone) and is divided in three steps: microwave assisted extraction, purification by solid phase extraction and assay by ultra high performance liquid chromatography (UHPLC). Each step was included in the validation protocol and the accuracy profiles methodology was used to fully validate the method. The method was fully validated between 0.25 mg and 5 mg pachyrrhizin per gram of seeds and between 0.58 mg/g and 4 mg/g for rotenone. More than one hundred samples from different accessions, locations of growth and harvest dates were screened. Pachyrrhizine concentrations ranged from 3.29 mg/g to lower than 0.25 mg/g while rotenone concentrations ranged from 3.53 mg/g to lower than 0.58 mg/g. This screening along with principal component analysis (PCA) and discriminant analysis (DA) analyses allowed the selection of the more interesting genotypes in terms of low concentrations of these two toxic polyphenols.

Measurement uncertainty in pharmaceutical analysis and its application

The measurement uncertainty provides complete information about an analytical result. This is very important because several decisions of compliance or non-compliance are based on analytical results in pharmaceutical industries. The aim of this work was to evaluate and discuss the estimation of uncertainty in pharmaceutical analysis. The uncertainty is a useful tool in the assessment of compliance or non-compliance of in-process and final pharmaceutical products as well as in the assessment of pharmaceutical equivalence and stability study of drug products.

Reply to the responses on the comments on "Uncertainty profiles for the validation of analytical methods" by Saffaj and Ihssane

Saffaj and Ihssane, recently proposed an uncertainty profile for evaluating the validity of analytical methods using the statistical methodology of γ-confidence β-content tolerance intervals. This profile assesses the validity of the method by comparing the method measurement uncertainty to a predefined acceptance limit stating the maximum uncertainty suitable for the method under study. In this letter we comment on the response (T. Saffaj, B. Ihssane, Talanta 94 (2012) 361-362) these authors have made to our previous letter (E. Rozet, E. Ziemons, R.D. Marini, B. Boulanger, Ph. Hubert, Talanta 88 (2012) 769-771). In particular, we demonstrate that β-expectation tolerance intervals are prediction intervals, we show that β-expectation tolerance intervals are highly useful for assessing analytical methods validation and for estimating measurement uncertainty and finally we show what are the differences and implications for these two topics (validation and uncertainty) when using either the methodology of β-expectation tolerance intervals or the γ-confidence β-content tolerance tolerance interval one.