Importance of retention data from affinity and reverse-phase high-performance liquid chromatography on antitumor activity prediction of imidazoacridinones using QSAR strategy

Contribution of Reliable Chromatographic Data in QSAR for Modelling Bisphenol Transport across the Human Placenta Barrier

Regulatory measures and public concerns regarding bisphenol A (BPA) have led to its replacement by structural analogues, such as BPAF, BPAP, BPB, BPF, BPP, BPS, and BPZ. However, these alternatives are under surveillance for potential endocrine disruption, particularly during the critical period of fetal development. Despite their structural analogies, these BPs differ greatly in their placental transport efficiency. For predicting the fetal exposure of this important class of emerging contaminants, quantitative structure-activity relationship (QSAR) studies were developed to model and predict the placental clearance indices (CI). The most usual input parameters were molecular descriptors obtained by modelling, but for bisphenols (BPs) with structural similarities or heteroatoms such as sulfur, these descriptors do not contrast greatly. This study evaluated and compared the capacity of QSAR models based either on molecular or chromatographic descriptors or a combination of both to predict the placental passage of BPs. These chromatographic descriptors include both the retention mechanism and the peak shape on columns that reflect specific molecular interactions between solute and stationary and mobile phases and are characteristic of the molecular structure of BPs. The chromatographic peak shape such as the asymmetry and tailing factors had more influence on predicting the placental passage than the usual retention parameters. Furthermore, the QSAR model, having the best prediction capacity, was obtained with the chromatographic descriptors alone and met the criteria of internal and cross validation. These QSAR models are crucial for predicting the fetal exposure of this important class of emerging contaminants.

Chemometric approach to correlations between retention parameters of non-polar HPLC columns and physicochemical characteristics for ampholytic substances of biological and pharmaceutical relevance

The correlations between the retention parameters of forty ampholytic, biologically active and/or pharmaceutically relevant substances (obtained for three non-polar HPLC columns at various compositions of mobile phases and pH conditions: 2.5, 7.0, 11.5) and their thirty-two physicochemical (calculated/spectral) characteristics were investigated by applying chemometric methods of analysis. In three cases (among seven cases considered), Quantitative Property-Retention Relation (QPRR) models meeting the predictive capability criteria were developed (the values of R², Q²_CV, Q²_Ext were higher than 0.76, 0.66 and 0.67, respectively, while values of RMSE_C, RMSE_CV and RMSE_Ext were lower than 0.51, 0.65 and 0.65 in each developed model). These models create a useful platform for predicting retention parameters of untested chemicals and, to some extent, gaining pharmaceutically valuable information on the biologically active ampholytic substances based on their properties and the conditions of chromatographic separation.

Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds

Quantitative structure-retention relationship (QSRR) is a technique capable of improving the identification of analytes by predicting their retention time on a liquid chromatography column (LC) and/or their properties. This approach is particularly useful when LC is coupled with a high-resolution mass spectrometry (HRMS) platform. The main aim of the present study was to develop and describe appropriate QSRR models that provide usable predictive capability, allowing false positive identification to be removed during the interpretation of metabolomics data, while additionally increasing confidence of experimental results in doping control area. For this purpose, a dataset consisting of 146 drugs, metabolites and banned compounds from World Anti-Doping Agency (WADA) lists, was used. A QSRR study was carried out separately on high quality retention data determined by reversed-phase (RP-LC-HRMS) and hydrophilic interaction chromatography (HILIC-LC-HRMS) systems, employing a single protocol for each system. Multiple linear regression (MLR) was applied to construct the linear QSRR models based on a variety of theoretical molecular descriptors. The regression equations included a set of three descriptors for each model: ALogP, BELe6, R2p and ALogP(2), FDI, BLTA96, were used in the analysis of reversed-phase and HILIC column models, respectively. Statistically significant QSRR models (squared correlation coefficient for model fitting, R(2)=0.95 for RP and R(2)=0.84 for HILIC) indicate a strong correlation between retention time and the molecular descriptors. An evaluation of the best correlation models, performed by validation of each model using three tests (leave-one-out, leave-many-out, external tests), demonstrated the reliability of the models. This paper provides a practical and effective method for analytical chemists working with LC/HRMS platforms to improve predictive confidence of studies that seek to identify small molecules.