Design of Experiments (DoE) is an indispensable tool in contemporary drug analysis as it simultaneously balances a number of chromatographic parameters to ensure optimal separation in High Pressure Liquid Chromatography (HPLC). This manuscript briefly outlines the theoretical background of the DOE and provides step-by-step instruction for its implementation in HPLC pharmaceutical practice. It particularly discusses the classification of various design types and their possibilities to rationalize the different stages of HPLC method development workflow, such as the selection of the most influential factors, factors optimization and assessment of the method robustness. Additionally, the application of the DOE-based Analytical Quality by Design (AQbD) concept in the LC method development has been summarized. Recent achievements in the use of DOE in the development of stability-indicating LC and hyphenated LC-MS methods have also been briefly reported. Performing of Quantitative structure retention relationship (QSRR) study enhanced with DOE-based data collection was recomended as a future perspective in description of retention in HPLC system.
One-factor-at-a-time experimentation was used for a long time as gold-standard optimization for liquid chromatographic (LC) method development. This approach has two downsides as it requires a needlessly great number of experimental runs and it is unable to identify possible factor interactions. At the end of the last century, however, this problem could be solved with the introduction of new chemometric strategies. This chapter aims at presenting quantitative structure–retention relationship (QSRR) models with structuring possibilities, from the point of feature selection through various machine learning algorithms that can be used in model building, for internal and external validation of the proposed models. The presented strategies of QSRR model can be a good starting point for analysts to use and adopt them as a good practice for their applications. QSRR models can be used in predicting the retention behavior of compounds, to point out the molecular features governing the retention, and consequently to gain insight into the retention mechanisms. In terms of these applications, special attention was drawn to modified chromatographic systems, characterized by mobile or stationary phase modifications. Although chromatographic methods are applied in a wide variety of fields, the greatest attention has been devoted to the analysis of pharmaceuticals.
An alternative to the time-consuming and error-prone pharmacopoeial gas chromatography method for the analysis of fatty acids (FAs) is urgently needed. The objective was therefore to propose a robust liquid chromatography method with charged aerosol detection for the analysis of polysorbate 80 (PS80) and magnesium stearate. FAs with different numbers of carbon atoms in the chain necessitated the use of a gradient method with a Hypersil Gold C18 column and acetonitrile as organic modifier. The risk-based Analytical Quality by Design approach was applied to define the Method Operable Design Region (MODR). Formic acid concentration, initial and final percentages of acetonitrile, gradient elution time, column temperature, and mobile phase flow rate were identified as critical method parameters (CMPs). The initial and final percentages of acetonitrile were fixed while the remaining CMPs were fine-tuned using response surface methodology. Critical method attributes included the baseline separation of adjacent peaks (α-linolenic and myristic acid, and oleic and petroselinic acid) and the retention factor of the last compound eluted, stearic acid. The MODR was calculated by Monte Carlo simulations with a probability equal or greater than 90%. Finally, the column temperature was set at 33 °C, the flow rate was 0.575 mL/min, and acetonitrile linearly increased from 70 to 80% (v/v) within 14.2 min.
New optimization strategy based on mixed Quantitative Structure-Retention Relationship (QSRR) model was proposed for improving the RP-HPLC separation of aripiprazole and its impurities (IMP A-E). Firstly, experimental parameters (EPs) (mobile phase composition and flow rate) were varied according to Box-Behnken Design and afterwards, artificial neural network (ANN) as QSRR model was built correlating EPs and selected molecular descriptors (ovality, torsion energy and non-1,4-Van der Waals energy) with analytes log-transformed retention time. Values of root mean square error (RMSE) were used for ANNs quality estimation (0.0227, 0.0191 and 0.0230 for training, verification and test set, respectively). Separations of critical peak pairs on chromatogram (IMP A-B and IMP D-C) were optimized using ANNs for which EPs served as inputs and log-transformed separation criteria s as outputs. They were validated applying leave-one-out cross-validation (RMSE values 0.065 and 0.056, respectively). Obtained ANNs were used for plotting response surfaces upon which analyses chromatographic conditions resulting in optimal analytes retention behaviour and optimal values of separation criteria s were defined and they comprised of 54 % of methanol at the beginning and 79 % of methanol at the end of gradient elution programme with mobile phase flow rate of 460 ?L min-1.
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