Solute descriptors for characterizing retention properties of open-tubular columns of different selectivity in gas chromatography at intermediate temperatures

Determination of solvation parameter model compound descriptors by gas chromatography

The solvation parameter model uses five system independent descriptors to characterize compound properties defined as excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity, A, hydrogen-bond basicity, B, and the gas-liquid partition constant at 25 °C on n-hexadecane, L, to model transfer properties in gas-condensed phase biphasic systems. The E descriptor for compounds liquid at 20 °C is available by calculation using a refractive index value while E for solid compounds at 20 °C and the S, A, B, and L descriptors are determined by experiment. As a single-technique approach, it is shown that with up to 20 retention factor measurements on four columns comprising a poly(siloxane) containing methyloctyl or dimethyldiphenylsiloxane monomers (SPB-Octyl or HP-5), a poly(siloxane) containing methyltrifluoropropylsiloxane monomers (Rtx-OPP or DB-210), a poly(siloxane) containing bis(cyanopropylsiloxane) monomers (HP-88 or SGE BPX-90), and a poly(ethylene glycol) stationary phase (DB-WAXetr or HP-INNOWAX) are suitable for assigning the S, A, and L descriptors. Using the descriptors in the updated WSU compound descriptor database as target values the average absolute error in the descriptor assignments for 52 varied compounds in the temperature range 60-140 °C was 0.072 for E, 0.016 for S, 0.008 for A, and 0.022 for L corresponding to 30 %, 3.5 %, and 0.6 % as a relative average absolute error for E, S, and L, respectively. For the higher temperature range of 160-240 °C and 34 varied compounds that are liquid at 20 °C the average absolute error for the S, A and L descriptors was 0.026, 0.020, and 0.031, respectively, with the largest relative average absolute error for S of 3.2 % (< 1 % for the L descriptor). For 35 varied compounds that are solid at 20 °C the relative absolute error for the E, S, A, and L descriptors in the higher temperature range was 0.068, 0.035, 0.020, and 0.020, respectively, with a relative average absolute error for E (6.5 %), S (3.5 %) and L (0.88 %). The S, A, and L descriptor can be accurately assigned on the four-column system over a wide temperature range. The E descriptor for solid compounds at 20 °C exhibits greater variability than desirable. The B descriptor cannot be assigned by the four-column system, which lack hydrogen-bond acid functional groups, and is only poorly assigned on the weak hydrogen-bond acid ionic liquid column SLB-IL100.

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.

Reliable prediction of sensory irritation threshold values of organic compounds using new models based on linear free energy relationships and GC×GC retention parameters

The human sensory irritation threshold (SIT) is an important biochemical parameter for the exposure assessment of organic air pollutants. First, we recalibrated the Abraham solvation models (ASMs) for 9 SIT endpoints by curating 720 individual experimental SIT values to find an accurate and parsimonious ASM variant, which exhibited root mean square error (RMSE) = 0.174-0.473 log unit. Second, we report linear free energy relationships - henceforth called partition models (PMs) - which exploit the correlations of 9 SIT endpoints with the linear combinations of partition coefficients for octanol-water and air-water systems showing RMSE = 0.221-0.591 log unit. These PMs can easily be integrated into widely used EPI-Suite™ screening tool. The explanatory and predictive performance of PMs were like parameter-intensive ASMs. Third, we present GC × GC models that are based on the retention times of the nonpolar analytes on the comprehensive two-dimensional gas chromatography (GC × GC), which successfully described the SIT variance (R²=0.959-0.996) and depicted a strong predictive power (RMSE = 0.359-0.660 log unit) for an independent set of nonpolar analytes. Taken together, PMs allow easy SIT screening of organic chemicals compared to ASMs. Unlike ASMs, our GC × GC models can be applied to estimate SIT of complex nonpolar mixtures.

What experimental factors influence the accuracy of retention projections in gas chromatography-mass spectrometry?

Programmed-temperature gas chromatographic (GC) retention information is difficult to share because it depends on so many experimental factors that vary among laboratories. Though linear retention indexing cannot properly account for experimental differences, retention times can be accurately calculated, or "projected", from shared isothermal retention vs. temperature (T) relationships, but only if the temperature program and hold-up time vs. T profile produced by a GC is known with great precision. The effort required to measure these profiles were previously impractical, but we recently showed that they can be easily back-calculated from the programmed-temperature retention times of a set of 25 n-alkanes using open-source software at www.retentionprediction.org/gc. In a multi-lab study, the approach was shown to account for both intentional and unintentional differences in the temperature programs, flow rates, and inlet pressures produced by the GCs. Here, we tested 16 other experimental factors and found that only 5 could reduce accuracy in retention projections: injection history, exposure to very high levels of oxygen at high temperature, a very low transfer line temperature, an overloaded column, and a very short column (≤15m). We find that the retention projection methodology acts as a hybrid of conventional retention projection and retention indexing, drawing on the advantages of both; it properly accounts for a wide range of experimental conditions while accommodating the effects of experimental factors not properly taken into account in the calculations. Finally, we developed a four-step protocol to efficiently troubleshoot a GC system after it is found to be yielding inaccurate retention projections.

A practical methodology to measure unbiased gas chromatographic retention factor vs. temperature relationships

Compound identification continues to be a major challenge. Gas chromatography-mass spectrometry (GC-MS) is a primary tool used for this purpose, but the GC retention information it provides is underutilized because existing retention databases are experimentally restrictive and unreliable. A methodology called "retention projection" has the potential to overcome these limitations, but it requires the retention factor (k) vs. T relationship of a compound to calculate its retention time. Direct methods of measuring k vs. T relationships from a series of isothermal runs are tedious and time-consuming. Instead, a series of temperature programs can be used to quickly measure the k vs. T relationships, but they are generally not as accurate when measured this way because they are strongly biased by non-ideal behavior of the GC system in each of the runs. In this work, we overcome that problem by using the retention times of 25 n-alkanes to back-calculate the effective temperature profile and hold-up time vs. T profiles produced in each of the six temperature programs. When the profiles were measured this way and taken into account, the k vs. T relationships measured from each of two different GC-MS instruments were nearly as accurate as the ones measured isothermally, showing less than two-fold more error. Furthermore, temperature-programmed retention times calculated in five other laboratories from the new k vs. T relationships had the same distribution of error as when they were calculated from k vs. T relationships measured isothermally. Free software was developed to make the methodology easy to use. The new methodology potentially provides a relatively fast and easy way to measure unbiased k vs. T relationships.

Applications of polyparameter linear free energy relationships in environmental chemistry

Partitioning behavior of organic chemicals has tremendous influences on their environmental distribution, reaction rates, bioaccumulation, and toxic effects. Polyparameter linear free energy relationships (PP-LFERs) have been proven to be useful to characterize the equilibrium partitioning of organic chemicals in various environmental and technical partitioning systems and predict the respective partition coefficients. Over the past decade, PP-LFER solute descriptors for numerous environmentally relevant organic chemicals and system parameters for environmentally important partitioning systems have been determined, extending substantially the applicability of the PP-LFER approaches. However, the information needed for the use of PP-LFERs including descriptors and parameters is scattered over a large number of publications. In this work, we review the state of the art of the PP-LFER approaches in environmental chemical applications. The solute descriptors and system parameters reported in the literature and the availability of their database are summarized, and their calibration and prediction methods are overviewed. We also describe tips and pitfalls associated with the use of the PP-LFER approaches and identify research needs to improve further the usefulness of PP-LFERs for environmental chemistry.

Mapping environmental partitioning properties of nonpolar complex mixtures by use of GC × GC

Comprehensive two-dimensional gas chromatography (GC × GC) is effective for separating and quantifying nonpolar organic chemicals in complex mixtures. Here we present a model to estimate 11 environmental partitioning properties for nonpolar analytes based on GC × GC chromatogram retention time information. The considered partitioning properties span several phases including pure liquid, air, water, octanol, hexadecane, particle natural organic matter, dissolved organic matter, and organism lipids. The model training set and test sets are based on a literature compilation of 648 individual experimental partitioning property data. For a test set of 50 nonpolar environmental contaminants, predicted partition coefficients exhibit root-mean-squared errors ranging from 0.19 to 0.48 log unit, outperforming Abraham-type solvation models for the same chemical set. The approach is applicable to nonpolar organic chemicals containing C, H, F, Cl, Br, and I, having boiling points ≤402 °C. The presented model is calibrated, easy to apply, and requires the user only to identify a small set of known analytes that adapt the model to the GC × GC instrument program. The analyst can thus map partitioning property estimates onto GC × GC chromatograms of complex mixtures. For example, analyzed nonpolar chemicals can be screened for long-range transport potential, aquatic bioaccumulation potential, arctic contamination potential, and other characteristic partitioning behaviors.

Experimental determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for pesticides and other contaminants: new measurements and recommendations

Well-calibrated polyparameter linear free energy relationships (pp-LFERs) are an accurate way to predict partition coefficients (K) for neutral organic chemicals. In this work, pp-LFER substance descriptors of 111 environmentally relevant substances, mainly pesticides, were determined experimentally using gas chromatographic (GC) retention times and liquid/liquid partition coefficients. The complete set of descriptors for 50 compounds are being reported here for the first time. Validation of the measured substance descriptors was done by comparing predicted and experimental log K for the systems octanol/water (Kow), water/air (Kwa), and organic carbon/water (Koc), all of which indicated a high reliability of pp-LFER predictions based on the determined descriptors (e.g., a root mean squared error of 0.39 for log Kow). The descriptors presented in this work in combination with existing pp-LFER system equations substantially extend (and in some cases correct) our knowledge on partition properties of these 111 chemicals. In addition, the results of this work provide insight on some general guidelines with respect to the method combination best suited for deriving descriptors for environmentally relevant compounds.

"Retention projection" enables reliable use of shared gas chromatographic retention data across laboratories, instruments, and methods

Gas chromatography/mass spectrometry (GC/MS) is a primary tool used to identify compounds in complex samples. Both mass spectra and GC retention times are matched to those of standards; however, it is often impractical to have standards on hand for every compound of interest, so we must rely on shared databases of MS data and GC retention information. Unfortunately, retention databases (e.g., linear retention index libraries) are experimentally restrictive, notoriously unreliable, and strongly instrument dependent, relegating GC retention information to a minor, often negligible role in compound identification despite its potential power. A new methodology called "retention projection" has great potential to overcome the limitations of shared chromatographic databases. In this work, we tested the reliability of the methodology in five independent laboratories. We found that, even when each lab ran nominally the same method, the methodology was 3-fold more accurate than retention indexing because it properly accounted for unintentional differences between the GC/MS systems. When the laboratories used different methods of their own choosing, retention projections were 4- to 165-fold more accurate. More importantly, the distribution of error in the retention projections was predictable across different methods and laboratories, thus enabling automatic calculation of retention time tolerance windows. Tolerance windows at 99% confidence were generally narrower than those widely used even when physical standards are on hand to measure their retention. With its high accuracy and reliability, the new retention projection methodology makes GC retention a reliable, precise tool for compound identification, even when standards are not available to the user.

Easy and accurate calculation of programmed temperature gas chromatographic retention times by back-calculation of temperature and hold-up time profiles

Linear retention indices are commonly used to identify compounds in programmed-temperature gas chromatography (GC), but they are unreliable unless the original experimental conditions used to measure them are stringently reproduced. However, differences in many experimental conditions may be properly taken into account by calculating programmed-temperature retention times of compounds from their measured isothermal retention vs. temperature relationships. We call this approach "retention projection". Until now, retention projection has been impractical because it required very precise, meticulous measurement of the temperature vs. time and hold-up time vs. temperature profiles actually produced by a specific GC instrument to be accurate. Here we present a new, easy-to-use methodology to precisely measure those profiles: we spike a sample with 25 n-alkanes and use their measured, programmed-temperature retention times to precisely back-calculate what the instrument profiles must have been. Then, when we use those back-calculated profiles to project retention times of 63 chemically diverse compounds, we found that the projections are extremely accurate (e.g. to ±0.9 s in a 40 min ramp). They remained accurate with different temperature programs, GC instruments, inlet pressures, flow rates, and with columns taken from different batches of stationary phase while the accuracy of retention indices became worse the more the experimental conditions were changed from the original ones used to measure them. We also developed new, open-source software (http://www.retentionprediction.org/gc) to demonstrate the system.

Predicting sorption of pesticides and other multifunctional organic chemicals to soil organic carbon

Chemicals of current environmental concern are often multifunctional and more polar and more complex than classical pollutants such as polychlorinated biphenyls (PCB) or polycyclic aromatic hydrocarbons (PAH). Traditional models for predicting the partitioning in the environment such as group contribution methods or correlations with octanol-water partitioning cannot be expected to work well for such complex chemicals. In contrast, poly parameter Linear Free Energy Relationships (pp-LFERs) have been proven to describe partitioning of polar and nonpolar chemicals in all kinds of sorbing systems. Here, a pp-LFER model for soil-water partitioning was calibrated with data for 79 polar and nonpolar compounds that cover a very wide range of the relevant intermolecular interactions. The data set used for the model calibration in this work is more diverse and covers a wider range of the chemical space than other pp-LFERs published so far. Subsequently, the experimental data for about 50 pesticides and pharmaceuticals -not involved in the model calibration- were used as independent validation of this new calibrated model. The model performs well with a standard error of 0.25 log units for fitting the calibration data and with a root-mean-square error of 0.4 log units for the pesticides and pharmaceuticals. The validation with the independent data set for pesticides and pharmaceuticals also shows that the pp-LFER model reported here performs better compared to earlier published pp-LFER models and to the traditional log Kow correlation.

Partitioning of organochlorine pesticides from water to polyethylene passive samplers

The mass transfer rates and equilibrium partitioning behaviour of 14 diverse organochlorine pesticides (OCP) between water and polyethylene (PE) passive samplers, cut from custom made PE sheets and commercial polyethylene plastic bags, were quantified. Overall mass transfer coefficients, k(O), estimated PE membrane diffusion coefficients, D(PE), and PE-water partitioning coefficients, K(PE-water,) are reported. In addition, the partitioning of three polycyclic aromatic hydrocarbons (PAHs) from water to PE is quantified and compared with literature values. K(PE-water) values agreed mostly within a factor of two for both passive samplers and also with literature values for the reference PAHs. As PE is expected to exhibit similar sorption behaviour to long-chain alkanes, PE-water partitioning coefficients were compared to hexadecane-water partitioning coefficients estimated with the SPARC online calculator, COSMOtherm and a polyparameter linear free energy relationship based on the Abraham approach. The best correlation for all compounds tested was with COSMOtherm estimated hexadecane-water partitioning coefficients.