Assessment of the orthogonality in two-dimensional separation systems using criteria defined by the maximal information coefficient

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

A multidimensional chromatography/high-resolution mass spectrometry approach for the in-depth metabolites characterization of two Astragalus species

To address the chemical complexity is indispensable in a number of research fields. Herb metabolome is typically composed by more than one class of structure analogs produced via different biosynthetic pathways. Multidimensional chromatography (MDC), due to the greatly enhanced separation space, offers the potential solution to comprehensive characterization of herbal metabolites. Here, we presented a strategy, by integrating MDC and quadrupole time-of-flight mass spectrometry (QTOF-MS), to accomplish the in-depth herbal metabolites characterization. Using the metabolome of two Astragalus species (A. membranaceus var. mongholicus,AMM; A. membranaceus, AM) as the case, an off-line three-dimensional liquid chromatography (3D-LC) system was established: hydrophilic interaction chromatography using an XAmide column as the first dimension (¹D) for fractionating the total extract, on-line reversed-phase × reversed-phase liquid chromatography separately configuring a CSH Fluoro-Phenyl column and a Cosmocore C18 column as the second dimension (²D) and the third dimension (³D) of chromatography to enable the explicit separation of three well fractionated samples. Moreover, the negative-mode collision-induced dissociation by QTOF-MS under the optimized condition could provide diversified fragments that were useful for the structural elucidation of AMM and AM. An in-house library (composed by 247 known compounds) and comparison with 43 reference standards were utilized to assist more reliable characterization. We could characterize 513 compounds from two Astragalus species (344 from AMM and 323 from AM), including 236 flavonoids, 150 triterpenoids, 18 organic acids, and 109 others. Conclusively, the established MDC approach gained excellent performance favoring the analogs-oriented in-depth characterization of herbal metabolites, but received uncompromising analytical efficiency.

Systematic Evaluation of Liquid Chromatography (LC) Column Combinations for Application in Two-Dimensional LC Metabolomic Studies

In comparison to proteomics, the application of two-dimensional liquid chromatography (2D LC) in the field of metabolomics is still premature. One reason might be the elevated chemical complexity and the associated challenge of selecting proper separation conditions in each dimension. As orthogonality of dimensions is a major issue, the present study aimed for the identification of successful stationary phase combinations. To determine the degree of orthogonality, first, six different metrics, namely, Pearson's correlation coefficient (1 - |R|), the nearest-neighbor distances (H̅_NND), the "asterisk equations" (A_O), and surface coverage by bins (SC_G), convex hulls (SC_CH), and α-convex hulls (SC_αH), were critically assessed by 15 artificial 2D data sets, and a systematic parameter optimization of α-convex hulls was conducted. SG_G, SC_αH with α = 0.1, and H̅_NND generated valid results with sensitivity toward space utilization and data distribution and, therefore, were applied to pairs of experimental retention time sets obtained for >350 metabolites, selected to represent the chemical space of human urine. Normalized retention data were obtained for 23 chromatographic setups, comprising reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), and mixed-mode separation systems with an ion exchange (IEX) contribution. As expected, no single LC setting provided separation of all considered analytes, but while conventional RP×HILIC combinations appeared rather complementary than orthogonal, the incorporation of IEX properties into the RP dimension substantially increased the 2D potential. Eventually, one of the most promising column combinations was implemented for an offline 2D LC time-of-flight mass spectrometry analysis of a lyophilized urine sample. Targeted screening resulted in a total of 164 detected metabolites and confirmed the outstanding coverage of the 2D retention space.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Research on Hybrid Feature Selection Method Based on Iterative Approximation Markov Blanket

The basic experimental data of traditional Chinese medicine are generally obtained by high-performance liquid chromatography and mass spectrometry. The data often show the characteristics of high dimensionality and few samples, and there are many irrelevant features and redundant features in the data, which bring challenges to the in-depth exploration of Chinese medicine material information. A hybrid feature selection method based on iterative approximate Markov blanket (CI_AMB) is proposed in the paper. The method uses the maximum information coefficient to measure the correlation between features and target variables and achieves the purpose of filtering irrelevant features according to the evaluation criteria, firstly. The iterative approximation Markov blanket strategy analyzes the redundancy between features and implements the elimination of redundant features and then selects an effective feature subset finally. Comparative experiments using traditional Chinese medicine material basic experimental data and UCI's multiple public datasets show that the new method has a better advantage to select a small number of highly explanatory features, compared with Lasso, XGBoost, and the classic approximate Markov blanket method.

A novel two-dimensional liquid chromatography system for the simultaneous determination of three monoterpene indole alkaloids in biological matrices

The present paper describes a novel two-dimensional liquid chromatography (2D-LC) system, which is comprised of a first-dimensional ion exchange chromatography (IEX1) column, trap column, and second-dimensional reversed-phase chromatography (RP2) column system. The biological sample is separated by the first-dimensional LC using an IEX column to remove interferences. The analytes are transferred to the trap column after heart-cutting. Then, the analytes are transferred to the second-dimensional LC using an RP2 column for further separation and ultraviolet detection. This 2D-LC system can offer a large injection volume to provide sufficient sensitivity and exhibits a strong capacity for removing interferences. Here, the determination of three monoterpene indole alkaloids (MIAs; gelsemine, koumine, and humantenmine) from Gelsemium in biological matrices (plasma, tissue, and urine) was used this 2D-LC system. After a rapid and easy sample preparation method based on protein precipitation, the sample was injected into the 2D-LC. The method was developed and validated in terms of the selectivity, LOD, LOQ, linearity, precision, accuracy, and stability. The sample preparation time for the three MIAs was 15 min. The LOD for these compounds was 10 ng/mL, which was lower than the developed HPLC methods. The results showed that this method had good quantitation performance and allowed the determination of gelsemine, koumine, and humantenmine in biological matrices. The method is rapid, exhibits high selectivity, has good sensitivity, and is low-cost, thus making it well-suited for application in the pharmaceutical and toxicological analysis of Gelsemium. Graphical abstract.