Optimization of flow splitting and make-up flow conditions in liquid chromatography/electrospray ionization mass spectrometry

Use of N-(4-aminophenyl)piperidine derivatization to improve organic acid detection with supercritical fluid chromatography-mass spectrometry

The analysis of organic acids in complex mixtures by LC-MS can often prove challenging, especially due to the poor sensitivity of negative ionization mode required for detection of these compounds in their native (i.e., underivatized or untagged) form. These compounds have also been difficult to measure using supercritical fluid chromatography (SFC)-MS, a technique of growing importance for metabolomic analysis, with similar limitations based on negative ionization. In this report, the use of a high proton affinity N-(4-aminophenyl)piperidine derivatization tag is explored for the improvement of organic acid detection by SFC-MS. Four organic acids (lactic, succinic, malic, and citric acids) with varying numbers of carboxylate groups were derivatized with N-(4-aminophenyl)piperidine to achieve detection limits down to 0.5 ppb, with overall improvements in detection limit ranging from 25-to-2100-fold. The effect of the derivatization group on sensitivity, which increased by at least 200-fold for compounds that were detectable in their native form, and mass spectrometric detection are also described. Preliminary investigations into the separation of these derivatized compounds identified multiple stationary phases that could be used for complete separation of all four compounds by SFC. This derivatization technique provides an improved approach for the analysis of organic acids by SFC-MS, especially for those that are undetectable in their native form.

How to Couple LC-IRMS with HRMS─A Proof-of-Concept Study

Compound-specific stable isotope analysis (CSIA) is a unique analytical technique for determining small variations in isotope ratios of light isotopes in analytes from complex mixtures. A problem of CSIA using gas chromatography (GC) and liquid chromatography-isotope ratio mass spectrometry (LC-IRMS) is that any structural information of the analytes is lost due to the processes involved in determining the isotope ratio. To obtain the isotopic composition of, for example, carbon from organic compounds, all carbon in each analyte is quantitatively converted to CO₂. For GC-IRMS, open split GC-IRMS-MS couplings have been described that allow additional acquisition of structural information of analytes and interferences. Structural analysis using LC-IRMS is more difficult and requires additional technical and instrumental efforts. In this study, LC was combined for the first time with simultaneous analysis by IRMS and high-resolution mass spectrometry (HRMS), enabling the direct identification of unknown or coeluting species. We have thoroughly investigated and optimized the coupling and showed how technical problems, arising from instrumental conditions, can be overcome. To this end, it was successfully demonstrated that a consistent split ratio between IRMS and HRMS could be obtained using a variable postcolumn flow splitter. This coupling provided reproducible results in terms of resulting peak areas, isotope values, and retention time differences for the two mass spectrometer systems. To demonstrate the applicability of the coupling, we chose to address an important question regarding the purity of international isotope standards. In this context, we were able to confirm that the USGS41 reference material indeed contains substantial amounts of pyroglutamic acid as suggested previously in the literature. Moreover, the replacement material, USGS41a, still has significant amounts of pyroglutamic acid as impurity, rendering some caution necessary when using this material for isotopic calibration.

Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS

LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches- one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information.

Development of a sub-hour on-line comprehensive cation exchange chromatography x RPLC method hyphenated to HRMS for the characterization of lysine-linked antibody-drug conjugates

This study details the development of on-line two-dimensional liquid chromatography (2D-LC) methods combining cation-exchange chromatography (CEX) and reversed-phase liquid chromatography (RPLC) for the separation of the charge variants of a lysine-linked antibody-drug conjugate (ADC). This combination gives an excellent example of the potential benefits of 2D-LC approaches for the analysis of such complex protein formats. CEX is considered the reference technique for the separation of protein charge variants but its retention mechanism usually requires the use of a high concentration of non-volatile salts, which impedes its compatibility with MS detection. In this context, the use of an on-line 2D-LC-MS approach not only allows on-line desalting and indirect coupling of CEX with mass spectrometry (MS) detection but it also provides increased and complementary information within a single analysis. The first part of this study was devoted to the choice of stationary phases and the optimization of chromatographic conditions in both dimensions. Based on the results obtained in 1D-CEX with ultraviolet detection (UV) and 1D-RPLC with UV and high-resolution mass spectrometry (HRMS) detections, an on-line comprehensive two-dimensional liquid chromatography method combining CEX and RPLC was developed. The last part of this study was devoted to the identification of the separated species using HRMS detection and in the comparison of three ADC samples exposed to different durations of thermal stress.

Towards a generic method for ion chromatography/mass spectrometry of low-molecular-weight amines in pharmaceutical drug discovery and development

RATIONALE

Low-molecular-weight amines are encountered in pharmaceutical analysis, e.g. as reactants in chemical syntheses, but are challenging to analyse using ultrahigh-performance liquid chromatography/mass spectrometry (UHPLC/MS) due to their high polarity causing poor retention. Ion chromatography/mass spectrometry (IC/MS) is an emerging technique for polar molecule analysis that offers better separation. A generic IC/MS method would overcome problems associated with using UHPLC/MS in drug discovery and development environments.

METHODS

Amine standards were analysed using IC/MS with gradient elution (variety of column temperatures evaluated). An electrospray ionisation (ESI) quadrupole mass spectrometer was operated in positive ion polarity in scanning mode. The make-up flow composition was evaluated by assessing the performance of a range of organic modifiers (acetonitrile, ethanol, methanol) and additives (acetic acid, formic acid, methanesulfonic acid). The ESI conditions were optimised to minimise adduct formation and promote generation of protonated molecules.

RESULTS

The performance attributes were investigated and optimised for low-molecular-weight amine analysis. Organic solvents and acidic additives were evaluated as make-up flow components to promote ESI, with 0.05% acetic acid in ethanol optimal for producing protonated molecules. The hydrogen bonding capability of amines led to abundant protonated molecule-solvent complexes; optimisation of source conditions reduced these, with collision-induced dissociation voltage having a strong effect. The detection limit was ≤1.78 ng for the amines analysed, which is fit-for-purpose for an open-access chemistry environment.

CONCLUSIONS

This study demonstrates the value of IC/MS for analysing low-molecular-weight amines. Good chromatographic separation of mixtures was possible without derivatisation. Ionisation efficiency was greatest using a make-up flow of 0.05% acetic acid in ethanol, and optimisation of ESI source conditions promoted protonated molecule generation for easy determination of molecular weight.

Quantification for non-targeted LC/MS screening without standard substances

Non-targeted and suspect analyses with liquid chromatography/electrospray/high-resolution mass spectrometry (LC/ESI/HRMS) are gaining importance as they enable identification of hundreds or even thousands of compounds in a single sample. Here, we present an approach to address the challenge to quantify compounds identified from LC/HRMS data without authentic standards. The approach uses random forest regression to predict the response of the compounds in ESI/HRMS with a mean error of 2.2 and 2.0 times for ESI positive and negative mode, respectively. We observe that the predicted responses can be transferred between different instruments via a regression approach. Furthermore, we applied the predicted responses to estimate the concentration of the compounds without the standard substances. The approach was validated by quantifying pesticides and mycotoxins in six different cereal samples. For applicability, the accuracy of the concentration prediction needs to be compatible with the effect (e.g. toxicology) predictions. We achieved the average quantification error of 5.4 times, which is well compatible with the accuracy of the toxicology predictions.

Innovative omics-based approaches for prioritisation and targeted isolation of natural products - new strategies for drug discovery

Covering: 2013 to 2019 The exploration of the chemical diversity of extracts from various biological sources has led to major drug discoveries. Over the past two decades, despite the introduction of advanced methodologies for natural product (NP) research (e.g., dereplication and high content screening), successful accounts of the validation of NPs as lead therapeutic candidates have been limited. In this context, one of the main challenges faced is related to working with crude natural extracts because of their complex composition and the inadequacies of classical bioguided isolation studies given the pace of high-throughput screening campaigns. In line with the development of metabolomics, genomics and chemometrics, significant advances in metabolite profiling have been achieved and have generated high-quality massive genome and metabolome data on natural extracts. The unambiguous identification of each individual NP in an extract using generic methods remains challenging. However, the establishment of structural links among NPs via molecular network analysis and the determination of common features of extract composition have provided invaluable information to the scientific community. In this context, new multi-informational-based profiling approaches integrating taxonomic and/or bioactivity data can hold promise for the discovery and development of new bioactive compounds and return NPs back to an exciting era of development. In this article, we examine recent studies that have the potential to improve the efficiency of NP prioritisation and to accelerate the targeted isolation of key NPs. Perspectives on the field's evolution are discussed.