Design of experiments for development and optimization of a liquid chromatography coupled to tandem mass spectrometry bioanalytical assay

Abstract

Design of experiments (DoE) is a valuable tool for the optimization of quantitative bioanalytical methods utilizing liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS). Liquid chromatography mass spectrometry (LC–MS) is composed of several processes, including, liquid introduction and analyte ionization. The goal is to transfer analytes from atmospheric pressure to vacuum and maintain conditions that are compatible for both LC and MS. These processes involve many experimental factors which need to be simultaneously optimized to obtain maximum sensitivity and resolution at minimum retention time. In this tutorial, the basic concepts of DoE will be explained with focus on practical use of DoE. Three case studies optimized with DoE for liquid chromatography tandem mass spectrometry (LC–MS/MS) quantitative assays will then be presented.

Chromatography Conditions Development by Design of Experiments for the Chemotype Differentiation of Four Bauhinia Species

“…Initially, a full factorial design was carried out (DoE—Step 1), in which all variables, their interactions, and levels were considered in the calculations. This design allows the screening of a high number of factors with fewer experiments and can also be used for robustness or ruggedness testing ( Thorsteinsdóttir & Thorsteinsdóttir, 2021 ). To meet this end, three critical method parameters that mainly affect chromatographic selectivity were considered: 1) chemistry of the stationary phase (column); 2) type of organic modifier, and 3) pH values.…”

“…The disadvantage of this strategy relies on the increased number of experiments and longer development time, especially when many parameters are affecting the separation. To circumvent this, the use of the design of experiments (DoE) analytical approaches, which systematically vary multiple key variables (e.g., pH, temperature, organic modifier, stationary phase, among others) simultaneously to obtain suitable experimental conditions with a minimum number of experiments ( Tome et al, 2019 ; Thorsteinsdóttir & Thorsteinsdóttir, 2021 ). Screening and response-surface experimental designs allow the identification of significant factors, and the factor−response relationship is described by mathematical models, which can predict the optimal response.…”

Incompatibility of antimalarial drugs: challenges in formulating combination products for malaria

“…Screening is used to detect significant factors that may influence the outcome, and to identify the limits in which they ought to be investigated. After screening, a blend of factors that can yield optimal working conditions in a process called optimization have to be identified (Benredouane et al., 2016 ; Mhango et al., 2017 ; Thorsteinsdóttir & Thorsteinsdóttir, 2021 ). MODDE ® 13 (Sartorius, Umeå, Sweden) was used to design the experiment and perform statistical analysis.…”

Quality by Design Assisted Optimization of a Chiral Capillary Electrokinetic Chromatographic Method for the Separation of Amlodipine Enantiomers Using Maltodextrin as Chiral Selector

“…In previous studies, Tabani et al reported that maltodextrin with low DE (4-7) has a higher degree of stereoselectivity compared to DE (13)(14)(15)(16)(17) and DE (16.5-19.5) [10]. Its high aqueous solubility and low absorbance in the UV region allow MD to be employed at relatively high additive concentrations.…”

“…Contour surface plots can be used to visualize how the response is affected by factors and to find combinations of optimal conditions. Optimal designs, such as D-optimal designs allows for the identification of the critical factors and an interaction between variables with maximal information and a minimum number of trials [ 15 ]. This approach reduces the analysis time and results in a more efficient experiment.…”