Computer-Assisted Structure Identification (CASI)—An Automated Platform for High-Throughput Identification of Small Molecules by Two-Dimensional Gas Chromatography Coupled to Mass Spectrometry

Abstract

Compound identification is widely recognized as a major bottleneck for modern metabolomic approaches and high-throughput nontargeted characterization of complex matrices. To tackle this challenge, an automated platform entitled computer-assisted structure identification (CASI) was designed and developed in order to accelerate and standardize the identification of compound structures. In the first step of the process, CASI automatically searches mass spectral libraries for matches using a NIST MS Search algorithm, which proposes structural candidates for experimental spectra from two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOF-MS) measurements, each with an associated match factor. Next, quantitative structure-property relationship (QSPR) models implemented in CASI predict three specific parameters to enhance the confidence for correct compound identification, which were Kovats Index (KI) for the first dimension (1D) separation, relative retention time for the second dimension separation (2DrelRT) and boiling point (BP). In order to reduce the impact of chromatographic variability on the second dimension retention time, a concept based upon hypothetical reference points from linear regressions of a deuterated n-alkanes reference system was introduced, providing a more stable relative retention time measurement. Predicted values for KI and 2DrelRT were calculated and matched with experimentally derived values. Boiling points derived from 1D separations were matched with predicted boiling points, calculated from the chemical structures of the candidates. As a last step, CASI combines the NIST MS Search match factors (NIST MF) with up to three predicted parameter matches from the QSPR models to generate a combined CASI Score representing the measure of confidence for the identification. Threshold values were applied to the CASI Scores assigned to proposed structures, which improved the accuracy for the classification of true/false positives and true/false negatives. Results for the identification of compounds have been validated, and it has been demonstrated that identification using CASI is more accurate than using NIST MS Search alone. CASI is an easily accessible web-interfaced software platform which represents an innovative, high-throughput system that allows fast and accurate identification of constituents in complex matrices, such as those requiring 2D separation techniques.

Performance Evaluation of a Nontargeted Platform Using Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry Integrating Computer-Assisted Structure Identification and Automated Semiquantification for the Comprehensive Chemical Characterization of a Complex Matrix

“…Models to predict RI, 2DrelRT, and BP values for the Nonpolar method have been developed and reported previously. 22 DabsRT values were used. The descriptors used to create the models were tested with various learning algorithms (k-nearest neighbors, multiple linear regression, and support vector regression).…”

“…Confidence in Compound Identification. The CASI platform for GC × GC−TOFMS (published in this journal in 2013) 22 was core to performing high-throughput identification of the 2990 compounds (Supporting Information: Constituent List 3R4F) present in a combined TPM and GVP data set derived from the 3R4F reference cigarette. 22 The structural identities for a subset of 253 compounds were confirmed experimentally by the analysis of purchased reference standards.…”

Chromatographic retention indices in identification of chemical compounds

“…We do not discuss the various methods suggested for the prediction of RIs, but just mention several works where prediction was used for identification purposes [64][65][66][67][68][69].…”

Reduced exposure evaluation of an Electrically Heated Cigarette Smoking System. Part 1: Non-clinical and clinical insights

“…The HPHC yields of the MRTP should ideally, with the exception of nicotine, be below the HPHC yields in CC when expressed on a per mg nicotine basis. Special analytical techniques may be required to identify whether novel compounds are present in the smoke aerosol compared to CC (Knorr et al, 2011). (ii) Short-term clinical trials that are representative of 'actual use', i.e., no limitations in smoking rate, and subjects should be allowed to smoke their preferred brand in the CC group.…”