Factors affecting untargeted detection of doping agents in biological samples.

Doping control is essential for sports, and untargeted detection of doping agents (UDDA) is the holy grail for anti-doping strategies. The present study examined major factors impacting UDDA with metabolomic data processing, including the use of blank samples, signal-to-noise ratio thresholds, and the minimum chromatographic peak intensity. Contrary to data processing in metabolomics studies, both blank sample use (either blank solvent or plasma) and marking of background compounds were found to be unnecessary for UDDA in biological samples, the first such report to the authors' knowledge. The minimum peak intensity required to detect chromatographic peaks affected the limit of detection (LOD) and data processing time for untargeted detection of 57 drugs spiked into equine plasma. The ratio of the mean (ROM) of the extracted ion chromatographic peak area of a compound in the sample group (SG) to that in the control group (CG) impacted its LOD, and a small ROM value such as 2 is recommended for UDDA. Mathematical modeling of the required signal-to-noise ratio (S/N) for UDDA provided insights into the effect of the number of samples in the SG, the number of positive samples, and the ROM on the required S/N, highlighting the power of mathematics in addressing issues in analytical chemistry. The UDDA method was validated by its successful identification of untargeted doping agents in real-world post-competition equine plasma samples. This advancement in UDDA methodology will be a useful addition to the arsenal of approaches used to combat doping in sports.

Automated identification of unknown doping agents in confiscation samples by flow-injection mass spectrometry and mass spectral library searches.

Rapid and accurate identification of unknown compounds within suspicious samples confiscated for sports doping control and law enforcement drug testing is critical, but such analyses are often conducted manually and can be time-consuming. Here, we report a methodology for automated identification of unknown substances in confiscation samples by rapid automatic flow-injection analysis on a liquid chromatography coupled to high-resolution mass spectrometry system and identifying unknown compounds with Compound Discoverer software. The developed methodology was validated by comparing the automated identification results with those obtained from manual syringe-infusion experiments and manual tandem mass spectral library searches. The automated methodology resulted in far higher throughput and remarkably shorter turnaround time for analysis when compared with manual procedures and, in most cases, yielded more compounds. As this is the first such report to the authors' knowledge, this methodology may potentially transform analysis of confiscated samples in sports doping control and law enforcement drug testing.

Application of a semi-empirical dispersion correction for modeling water clusters.

The Grimme-D3 semi-empirical dispersion energy correction has been implemented for the original effective fragment potential for water (EFP1), and for systems that contain water molecules described by both correlated ab initio quantum mechanical (QM) molecules and EFP1. Binding energies obtained with these EFP1-D and QM/EFP1-D methods were tested using 27 benchmark species, including neutral, protonated, deprotonated, and auto-ionized water clusters and nine solute-water binary complexes. The EFP1-D and QM/EFP1-D binding energies are compared with those obtained using fully QM methods: second-order perturbation theory, and coupled cluster theory, CCSD(T), at the complete basis set (CBS) limit. The results show that the EFP1-D and QM/EFP1-D binding energies are in good agreement with CCSD(T)/CBS binding energies with a mean absolute error of 5.9 kcal/mol for water clusters and 0.8 kcal/mol for solute-water binary complexes. © 2018 Wiley Periodicals, Inc.