Exploring and modelling the responses of electrospray and atmospheric pressure chemical ionization techniques based on molecular descriptors

A Top Solvent-Additive-Ionization Technique Combination for Pesticides Direct Infusion MS Analysis

The leading type of ionization technique in mass spectrometry analyses is the ionization at atmospheric pressure. The aim of this study was to assess ESI and APCI ionization efficiency of pesticides introduced to the MS source in four organic phases, non-doped and doped with formic acid and ammonium formate. Ionization efficiency in modified ESI and APCI, applying in-source sample heating, was also assessed. The study was primarily designed to support non-chromatography-based mass spectrometry pesticides analysis by the direct infusion technique. Evaluation of analysis performances such as calibration performances, detectability, and sensitivity should indicate a top solvent-additive-source combination, leading to the highest ionization efficiency and lowest analytes detection limits.

Machine learning guided prediction of liquid chromatography-mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products

The limitation and control of genotoxic impurities (GTIs) has continued to receive attention from pharmaceutical companies and authorities for several decades. Because GTIs have the ability to damage deoxyribonucleic acid (DNA) and the potential to cause cancer, low-level quantitation is required to protect patients. A quick and easy method of determining the liquid chromatography-mass spectrometry (LC/MS) conditions for high-sensitivity analysis of GTIs may prospectively accelerate pharmaceutical development. In this study, a quantitative structure-property relationship (QSPR) model was developed for predicting the ionization efficiency of compounds using liquid-chromatography-mass spectrometry (LC/MS) parameters and molecular descriptors. Before implementing the QSPR prediction model, linear regression analysis was performed to model the relationship between the ionization efficiency and the LC/MS parameters for each compound. Comparison of the predicted peak areas with the experimentally observed peak areas showed good agreement based on the coefficient of determination (R² > 0.96). The machine learning-based QSPR approach begins with computation of the molecular descriptors expressing the physicochemical properties of a compound, followed by a genetic algorithm-based feature selection. Linear and nonlinear regression were performed, and support vector machine (SVM) was selected as the best machine learning algorithm for the prediction. The SVM algorithm was developed and optimized using 1031 experimental data points for nine compounds, including well-known GTIs. Validation of the model by comparison of the predicted and observed relative ionization efficiencies (RIE) showed a high coefficient of determination (R² = 0.96) and low root mean squared error value (RMSE = 0.118). Finally, this established prediction model was applied to hydrophilic interaction liquid chromatography coupled with MS for a new compound in new mobile phase compositions and new MS parameter settings. The RMSE of the predicted versus observed RIE was 0.203. This prediction accuracy was sufficient to determine the starting point of the LC/MS method development. The methodology demonstrated in this study can be used to determine the LC/MS conditions for high sensitivity analysis of GTIs.

Performance of the organic-diffusive gradients in thin-films passive sampler for measurement of target and suspect wastewater contaminants

Although passive sampling is widely accepted as an excellent tool for environmental monitoring, their integration with suspect or non-targeted screening by high-resolution mass spectrometry has been limited. This study describes the application of the organic-diffusive gradients in thin-films (o-DGT) passive sampler as a tool for accurate measurement of both targeted and suspect polar organic contaminants (primarily pharmaceuticals) in wastewater. First, performance of o-DGT was assessed alongside the polar organic chemical integrative sampler (POCIS) and active sampling at two wastewater treatment facilities using targeted analyses. Overall, water concentrations measured by o-DGT, POCIS, and 24-hr integrative active samples were in good agreement with each other. There were exceptions, including a systematic difference between o-DGT and POCIS at certain sites that we propose was a result of site-specific conditions and a difference in sampling rates between the two techniques. The second component of this work involved suspect screening of the o-DGT extracts using high-resolution, high mass accuracy quadrupole time-of-flight mass spectrometry (QTOF). Lamotrigine, venlafaxine, and des-methylvenlafaxine were three suspect compounds identified and selected as proof-of-concept case studies to determine the feasibility and accuracy of o-DGT for estimating water concentrations based upon predicted sampling rates using a previously validated o-DGT diffusion model. Semi-quantification of the suspect compounds was conducting using an average surrogate response factor based on the suite of compounds measured by the targeted analyses. This, combined with the modelled sampling rates provided time-weighted average wastewater concentrations of the identified suspects within a factor of 2 of the true value, confirmed by isotope dilution with mass labelled internal surrogates. To the knowledge of the authors, this work is the first to demonstrate the utility of the o-DGT passive sampler as a potential environmental screening tool that can be integrated into the rapidly advancing field of non-targeted high resolution mass spectrometry.

Ionization efficiency ladders as tools for choosing ionization mode and solvent in liquid chromatography/mass spectrometry

RATIONALE

The choice of mobile phase components and optimal ion source, mainly electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI), is a crucial part in liquid chromatography/mass spectrometry (LC/MS) method development to achieve higher sensitivity and lower detection limits. In this study we demonstrate how to rigorously solve these questions by using ionization efficiency scales.

METHODS

Four ionization efficiency scales are used: recorded with both APCI and ESI sources and using both methanol- and acetonitrile-containing mobile phases. Each scale contains altogether more than 50 compounds. In addition, measurements with a chromatographic column were also performed.

RESULTS

We observed a correlation between calibration graph slopes under LC conditions and logIE values in ESI (but not APCI) thereby validating the use of logIE values for choosing the ion source. Most of the studied compounds preferred ESI as an ion source and methanol as mobile organic phase. APCI remains the ion source of choice for polycyclic aromatic hydrocarbons. For APCI, both acetonitrile and methanol provide similar ionization efficiencies with few exceptions.

CONCLUSIONS

Overall the results of this work give a concise guideline for practitioners in choosing an ion source for LC/MS analysis on the basis of the chemical nature of the analytes.

A framework to estimate concentrations of potentially unknown substances by semi-quantification in liquid chromatography electrospray ionization mass spectrometry

Risk assessment of exposure to chemicals from food and other sources rely on quantitative information of the occurrence of these chemicals. As screening analysis is increasingly used, a strategy to semi-quantify unknown or untargeted analytes is required. A proof of concept strategy to semi-quantifying unknown substances in LC-MS was investigated by studying the responses of a chemically diverse marker set of 17 analytes using an experimental design study. Optimal conditions were established using two optimization parameters related to weak-responding compounds and to the overall response. All the 17 selected analytes were semi-quantified using a different analyte to assess the quantification performance under various conditions. It was found that source conditions had strong effects on the responses, with the range of low-response signals varying from -80% to over +300% compared to centerpoints. Positive electrospray (ESI+) was found to have more complex source interactions than negative electrospray (ESI-). Choice of quantification marker resulted in better quantification if the retention time difference was minimized (12 out of 12 cases error factor < 4.0) rather than if the accurate mass difference was minimized (7 out of 12 cases error factor < 4.0). Using optimal conditions and retention time selection, semi-quantification in ESI+ (70% quantified, average prediction error factor 2.08) and ESI- (100% quantified, average prediction error factor 1.74) yielded acceptable results for untargeted screening. The method was successfully applied to an extract of food contact material containing over 300 unknown substances. Without identification and authentic standards, the method was able to estimate the concentration of a virtually unlimited number of compounds thereby providing valuable data to prioritize compounds in risk assessment studies.

Electrospray Ionization Efficiency Is Dependent on Different Molecular Descriptors with Respect to Solvent pH and Instrumental Configuration

Over the past decades, electrospray ionization for mass spectrometry (ESI-MS) has become one of the most commonly employed techniques in analytical chemistry, mainly due to its broad applicability to polar and semipolar compounds and the superior selectivity which is achieved in combination with high resolution separation techniques. However, responsiveness of an analytical method also determines its suitability for the quantitation of chemical compounds; and in electrospray ionization for mass spectrometry, it can vary significantly among different analytes with identical solution concentrations. Therefore, we investigated the ESI-response behavior of 56 nitrogen-containing compounds including aromatic amines and pyridines, two compound classes of high importance to both, synthetic organic chemistry as well as to pharmaceutical sciences. These compounds are increasingly analyzed employing ESI mass spectrometry detection due to their polar, basic character. Signal intensities of the peaks from the protonated molecular ion (MH⁺) were acquired under different conditions and related to compound properties such as basicity, polarity, volatility and molecular size exploring their quantitative impact on ionization efficiency. As a result, we found that though solution basicity of a compound is the main factor initially determining the ESI response of the protonated molecular ion, other factors such as polarity and vaporability become more important under acidic solvent conditions and may nearly outweigh the importance of basicity under these conditions. Moreover, we show that different molecular descriptors may become important when using different types of instruments for such investigations, a fact not detailed so far in the available literature.

Virtual quantification of metabolites by capillary electrophoresis-electrospray ionization-mass spectrometry: predicting ionization efficiency without chemical standards

A major obstacle in metabolomics remains the identification and quantification of a large fraction of unknown metabolites in complex biological samples when purified standards are unavailable. Herein we introduce a multivariate strategy for de novo quantification of cationic/zwitterionic metabolites using capillary electrophoresis-electrospray ionization-mass spectrometry (CE-ESI-MS) based on fundamental molecular, thermodynamic, and electrokinetic properties of an ion. Multivariate calibration was used to derive a quantitative relationship between the measured relative response factor (RRF) of polar metabolites with respect to four physicochemical properties associated with ion evaporation in ESI-MS, namely, molecular volume (MV), octanol-water distribution coefficient (log D), absolute mobility (mu(o)), and effective charge (z(eff)). Our studies revealed that a limited set of intrinsic solute properties can be used to predict the RRF of various classes of metabolites (e.g., amino acids, amines, peptides, acylcarnitines, nucleosides, etc.) with reasonable accuracy and robustness provided that an appropriate training set is validated and ion responses are normalized to an internal standard(s). The applicability of the multivariate model to quantify micromolar levels of metabolites spiked in red blood cell (RBC) lysates was also examined by CE-ESI-MS without significant matrix effects caused by involatile salts and/or major co-ion interferences. This work demonstrates the feasibility for virtual quantification of low-abundance metabolites and their isomers in real-world samples using physicochemical properties estimated by computer modeling, while providing deeper insight into the wide disparity of solute responses in ESI-MS. New strategies for predicting ionization efficiency in silico allow for rapid and semiquantitative analysis of newly discovered biomarkers and/or drug metabolites in metabolomics research when chemical standards do not exist.