Quantitative structure–retention relationship studies with immobilized artificial membrane chromatography: II: Partial least squares regression

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R2 of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

Introduction Immobilized artificial membrane (IAM) chromatography is widely used in many aspects of drug discovery. It employs stationary phases, which contain phospholipids combining simulation of biological membranes with rapid measurements. Areas covered Advances in IAM stationary phases, chromatographic conditions and the underlying retention mechanism are discussed. The potential of IAM chromatography to model permeability and drug-membrane interactions as well as its use to estimate pharmacokinetic properties and toxicity endpoints including ecotoxicity, is outlined. Efforts to construct models for prediction IAM retention factors are presented. Expert opinion IAM chromatography, as a border case between partitioning and binding, has broadened its application from permeability studies to encompass processes involving tissue binding. Most IAM-based permeability models are hybrid models incorporating additional molecular descriptors, while for the estimation of pharmacokinetic properties and binding to off targets, IAM retention is combined with other biomimetic properties. However, for its integration into routine drug discovery protocols, reliable IAM prediction models implemented in relevant software should be developed, to enable its use in virtual screening and the design of new molecules. Conversely, preparation of new IAM columns with different phospholipids or mixed monomers offers enhanced flexibility and the potential to tailor the conditions according to the target property.

ABSTRACTIntroduction: The development of immobilized artificial membrane (IAM) chromatography has unfolded new perspectives for the use of chromatographic techniques in drug discovery, combining simulation of the environment of cell membranes with rapid measurements.Areas Covered: The present review describes the characteristics of phosphatidylcholine-based stationary phases and analyses the molecular factors governing IAM retention in comparison to n-octanol-water and liposomes partitioning systems as well as to reversed phase chromatography. Other biomimetic stationary phases are also briefly discussed. The potential of IAM chromatography to model permeability through the main physiological barriers and drug membrane interactions is outlined. Further applications to calculate complex pharmacokinetic properties, related to tissue binding, and to screen drug candidates for phospholipidosis, as well as to estimate cell accumulation/retention are surveyed.Expert opinion: The ambivalent nature of IAM chromatography, as a border case between passive diffusion and binding, defines its multiple potential applications. However, despite its successful performance in many permeability and drug-membrane interactions studies, IAM chromatography is still used as a supportive and not a stand-alone technique. Further studies looking at IAM chromatography in different biological processes are still required if this technique is to have a more focused and consistent application in drug discovery.

Correlation between chemical structure of non-congeneric solutes and their retention on polybutadiene-coated alumina

Relationship between immobilized artificial membrane chromatographic retention and human oral absorption of structurally diverse drugs

In the study, 18 antiepileptic hydantoin analogues were investigated by means of reversed-phase HPLC on C-18 stationary phase and eluent acetonitrile-water. Quantitative structure-retention relationship (QSRR) study has been applied in order to understand factors that affect the retention which is closely correlated to the activity (ED₅₀ values). To overview the compounds for similarities and dissimilarities principal component analysis (PCA) has been applied. Six multiple linear regression models based on the most relevant descriptors were developed. Descriptors for MLR were selected according to variable importance calculated by partial least squares (PLS) analysis. Besides ALOGP the most important is aromatic ratio for mobile phases with more than 45% of acetonitrile, as well as electrotopological states when the % of acetonitrile is less than 40%. High agreement between experimental and predicted retention, obtained in the validation procedure, indicated the good quality of the derived QSRR models. For individual linear models, crossvalidation squared correlation coefficients (Q²) ranging from 0.697 to 0.837 were obtained. The residual values (difference between observed and calculated) agreed well within experimental error. Additionally, models were compared in terms of the smallest residual value by recently developed method of ranking based on the sum of ranking differences (SRD).

A review of applications of the Immobilized Artificial Membrane (IAM) chromatography in drug discovery is given. IAM chromatography is presented as a tool to predict the interactions of solutes with biomembranes, blood-brain barrier permeability, volume of distribution, oral and skin absorption of drugs and compared to other in vitro techniques used to study drug bioavailability (caco-2 cells, liposome partition). Unbound phosphatidylcholine based stationary phases are also discussed. Some new trends and ideas in the IAM chromatography are presented.

Mechanism of separation on cholesterol–silica stationary phase for high-performance liquid chromatography as revealed by analysis of quantitative structure–retention relationships

Application of micellar liquid chromatography to model ecotoxicity of pesticides. Comparison with immobilized artificial membrane chromatography and n-octanol-water partitioning

Profiling Drug Membrane Transport via Immobilized Artificial Membrane Chromatography

Quantitative structure-retention relationship of selected imidazoline derivatives on α1-acid glycoprotein column

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl−, Mg2+ and SO42− was very high, that of CO32− was high and K+ was relatively lower, but HCO3− failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.

Comparative multiple quantitative structure–retention relationships modeling of gas chromatographic retention time of essential oils using multiple linear regression, principal component regression, and partial least squares techniques

Quantitative structure-retention relationships in the examination of the topography of the binding site of antihistamine drugs on α1-acid glycoprotein

Quantitative structure-retention relationships (QSRR) were used in this study to relate the chromatographic retention of different substituted coumarins to molecular structure. Different structural parameters were selected, such as topological, geometric, electronic, quantum-chemical and physico-chemical descriptors, in order to find an equation that fitted the chromatographic retention of these compounds. The method proposed by Dimov that classifies the descriptors in different groups in agreement with their values of correlation coefficients was analysed. Significant correlation equations were obtained with the following molecular descriptors: the total surface area (A T), the electrotopological state index (S(-o-)) of the oxygen in position 1 of coumarin, and the highest occupied molecular orbital energy (E HOMO), showing that the experimental retention, using stationary phases with low polarity, was related with the shape and electronic factors of the solutes. The models found have a good predictive ability as established by cross-validation r²cv values and thus, can be used to aid in the elucidation of the structure or the chromatographic retention of similar coumarins.