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.

High-pressure liquid chromatography (HPLC) is a technique of paramount importance in the analysis of pharmaceuticals because of its ability to separate moderately polar to less polar compounds, such as drugs and related substances.High-pressure liquid chromatography (HPLC) is a technique of paramount importance in the analysis of pharmaceuticals because of its ability to separate moderately polar to less polar compounds, such as drugs and related substances. The concept of green analytical chemistry (GAC) aims to provide more environmentally friendly and safer analytical methods in terms of reagents, energy, and waste. One of the major challenges of GAC is to find an appropriate approach to evaluate the greenness of analytical methods. An extension of GAC, called white analytical chemistry (WAC), has been introduced to consider not only environmental friendliness, but also other aspects that contribute to the sustainability of methods, such as analytical and economic or practical efficiency. HPLC methods are intrinsically not green, due to the high consumption of toxic organic solvents and the resulting generation of large amounts of toxic waste. Fortunately, there are many approaches to overcome the non-green character of HPLC methods. In this article, various modifications of the HPLC methods that increase its environmental friendliness are presented, as well as the various tools used to evaluate environmental friendliness. In addition, the new concept of white analytical chemistry is presented.

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.