Experimental design for the optimization and robustness testing of a liquid chromatography tandem mass spectrometry method for the trace analysis of the potentially genotoxic 1,3‐diisopropylurea

Abstract

This paper discusses a design of experiments (DoE) assisted optimization and robustness testing of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method development for the trace analysis of the potentially genotoxic 1,3-diisopropylurea (IPU) impurity in mometasone furoate glucocorticosteroid. Compared to the conventional trial-and-error method development, DoE is a cost-effective and systematic approach to system optimization by which the effects of multiple parameters and parameter interactions on a given response are considered. The LC and MS factors were studied simultaneously: flow (F), gradient (G), injection volume (Vinj), cone voltage (E(con)), and collision energy (E(col)). The optimization was carried out with respect to four responses: separation of peaks (Sep), peak area (A(p)), length of the analysis (T), and the signal-to-noise ratio (S/N). An optimization central composite face (CCF) DoE was conducted leading to the early discovery of carry-over effect which was further investigated in order to establish the maximum injectable sample load. A second DoE was conducted in order to obtain the optimal LC-MS/MS method. As part of the validation of the obtained method, its robustness was determined by conducting a fractional factorial of resolution III DoE, wherein column temperature and quadrupole resolution were considered as additional factors. The method utilizes a common Phenomenex Gemini NX C-18 HPLC analytical column with electrospray ionization and a triple quadrupole mass detector in multiple reaction monitoring (MRM) mode, resulting in short analyses with a 10-min runtime. The high sensitivity and low limit of quantification (LOQ) was achieved by (1) MRM mode (instead of single ion monitoring) and (2) avoiding the drawbacks of derivatization (incomplete reaction and time-consuming sample preparation). Quantitatively, the DoE method development strategy resulted in the robust trace analysis of IPU at 1.25 ng/mL absolute concentration corresponding to 0.25 ppm LOQ in 5 g/l mometasone furoate glucocorticosteroid. Validation was carried out in a linear range of 0.25-10 ppm and presented a relative standard deviation (RSD) of 1.08% for system precision. Regarding IPU recovery in mometasone furoate, spiked samples produced recoveries between 96 and 109 % in the range of 0.25 to 2 ppm.

Molecularly Imprinted Polymers for the Selective Extraction of Bisphenol A and Progesterone from Aqueous Media

“…The limit of detection (LOD) was obtained by calculating three times the standard deviation of the blanks divided by the slope of the curve. The limit of quantification (LOQ) was calculated to be three times the LOD [45,46]. To determine the calibration curves, all concentrations were measured in triplicate.…”

Tutorial review on validation of liquid chromatography–mass spectrometry methods: Part I

“…In the literature limited attention has been paid to varying besides the LC parameters also the MS parameters [146,147]. Our experience shows that in the case of LC-MS methods the MS parameters are as important as the LC parameters (although several problems, such as matrix effects due to overlapping peaks, analyte peaks falling outside the MS time window, etc are caused first of all by LC).…”

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

“…is technique involved performing actual experiments (rather than numerical experiments) at specific points in the design space as determined using the selected DOE and thereafter using the data to construct the relationships. e use of experimental-based design of experiments technique is a generally accepted procedure and has been used in various disciplines such as in membranes [24], processing of food and bioproducts [25], photovoltaics [26], biotechnology [27], and analytical chemistry [28,29]. In this work, using this technique will mitigate computation costs while ensuring that all conditions specific to the injection-molding unit are captured in the analysis and, thus, leading to more accurate results of the optimized parameters.…”