Gas Chromatography
Joseph Chamberlain in The Analysis of Drugs in Biological Fluids, 2018
Polar and active functional groups are converted to nonpolar and inert compounds, which is an ideal situation for successful gas chromatography. Some of the popular silylating agents are listed in Table 6.1. All these reagents are used to form the trimethylsilyl derivatives of the drug, the different reagents having different degrees of activity. Typically, the drug (or an extract containing the drug) is mixed with the reagent and a suitable solvent in a screw-capped vial fitted with a Teflon-faced septum. The reaction is usually instantaneous and the mixture can be sampled directly and injected into the gas chromatograph. As the derivatives are often unstable in the presence of water, the ploy of containing the sample in a sealed tube throughout ensures the derivatization remains complete. Trimethylsilylation is suitable for primary and secondary amines, alcohols and carboxylic acids. Fuller discussions on silylation reactions have been presented by Pierce484 and by Knapp.485
Production, Extraction and Characterization of Alginates from Seaweeds
Gokare A. Ravishankar, Ranga Rao Ambati in Handbook of Algal Technologies and Phytochemicals, 2019
Gaz Chromatography coupled with Mass Spectrometry and Electron Ionization (GC/MS-EI) is another method for determining M/G ratios of hydrolyzed alginates (trifluoroacetic acid 2 M, 90 min, 120°C) after derivatization of their constitutive uronic acids (Hentati et al. 2018). Silylation using BSTFA (Bis (trimethysilyl) trifluoroacetamide) and TMCS (trimethylchlorosilane) reagents (99:1–90:10) is the most common method used at various temperatures (from ambient temperature to 80°C) and reactions times (30 to 240 min). Trimethylsilyl-O-glycosides into dichloromethane can be separated for example into a OPTIMA-1MS column eluted with a helium flow rate (Hentati et al. 2018).
Radiochemistry for Preclinical Imaging Studies
George C. Kagadis, Nancy L. Ford, Dimitrios N. Karnabatidis, George K. Loudos in Handbook of Small Animal Imaging, 2018
Otherwise, tin (trialkylstannyl), silicon (trialkylsilyl), and boron (boronic acid) substituents have been incorporated in the precursor as leaving groups for radioiodination (Coenen et al. 2006; Adak et al. 2012). Accordingly, these reactions are called iodo-destannylation, iodo-desilylation, and iodo-deboronation. Still, to introduce these groups in the first place, the arene substrate usually needs to be activated. The subsequent iodination step, however, can be done at milder conditions and with high regiospecificity.
An overview of late-stage functionalization in today’s drug discovery
Published in Expert Opinion on Drug Discovery, 2019
Michael Moir, Jonathan J. Danon, Tristan A. Reekie, Michael Kassiou
The functionalization of aryl C–H bonds with main group reagents such as silanes and boranes occurs with unique regioselectivity dictated by the catalyst used. The obtained compounds are useful intermediates for further functionalization and diversification. The silylation of aryl C–H bonds is less developed than the corresponding borylation reactions. Despite their synthetic utility, reactions for the catalytic silylation of C–H bonds generally require harsh reaction conditions, excess of reagents and/or directing groups. Cheng and Hartwig have reported a method for the iridium catalyzed reaction of HSiMe(OSiMe3)2 and (hetero)arenes with high levels of sterically driven regioselectivity (Figure 4(c)) [46]. The silylation of a series of pharmaceutical compounds including clonidine (87% yield) demonstrates the applicability of this method for LSF.
Metabolism of cyclic phenones in rainbow trout in vitro assays
Published in Xenobiotica, 2020
Jose Serrano, Mark A. Tapper, Richard C. Kolanczyk, Barbara R. Sheedy, Tylor Lahren, Dean E. Hammermeister, Jeffrey S. Denny, Michael W. Hornung, Alena Kubátová, Patricia A. Kosian, Jessica Voelker, Patricia K. Schmieder
Efforts were made to search for metabolites of CPK in trout liver slice exposures. Despite the lack of metabolites detected for CPK in binding cytosols, the rapid decline in the amount of CPK/well in the presence of slices (Figure 2(c)), and the slice Vtg induction noted by Tapper et al. (2018b), indicated that metabolites were likely present. In fact, CPK yielded over 13 potential metabolites by 4 h of slice exposures, with none identified as CPKOH or opCPK for which Stds were available. Therefore, additional methods were necessary to characterize CPK metabolite structures as reported at length in Serrano et al. (2019). Nine of 13 possible metabolic products (labeled CPK M1–M9; cyclohexenyl-, cyclohexanone- and cyclohexanol-derivatives of CPK), could be readily characterized and measured in slice media above LOD. The low abundance of the other products prevented further characterization. The labels, structures, general classification and physical/experimental properties for the CPK metabolites identified in slice exposures are summarized in Table 3. Surprisingly, mass spectrometric evidence supported an apparent preference for metabolic modification of the cyclohexyl ring for all CPK metabolites characterized instead of a phenyl ring modification as predicted by metabolism models (Kolanczyk et al., 2012). Furthermore, chemical derivatization with silylation reagents supported that M6–M9 contained a hydroxyl group. As previously stated, there were no Stds available for M1–M9; therefore, it was not possible to directly quantify their concentrations in the slice or media. Thus, alternative strategies for semi-quantitation of M1–M9 in hexane extracts were applied (see Data analysis). Additional efforts were performed to measure the potential appearance of hydrophilic metabolites in aqueous fractions (see Supplemental Information). No evidence of any parent chemicals or metabolites was found in aqueous fractions of media or slice lysate analyzed by HPLC or LC-MS.
Arginine-mediated gut microbiome remodeling promotes host pulmonary immune defense against nontuberculous mycobacterial infection
Published in Gut Microbes, 2022
Young Jae Kim, June-Young Lee, Jae Jin Lee, Sang Min Jeon, Prashanta Silwal, In Soo Kim, Hyeon Ji Kim, Cho Rong Park, Chaeuk Chung, Jeong Eun Han, Jee-Won Choi, Euon Jung Tak, Ji-Ho Yoo, Su-Won Jeong, Do-Yeon Kim, Warisa Ketphan, Su-Young Kim, Byung Woo Jhun, Jake Whang, Jin-Man Kim, Hyungjin Eoh, Jin-Woo Bae, Eun-Kyeong Jo
Metabolite extraction and metabolome analysis were performed by MetaMass (Seoul, Korea). Briefly, 100 µL serum was added to 1 mL methanol containing internal standard (2-chloro-phenylalanine, 1 mg/mL). The mixture was vortexed for 1 min and sonicated for 10 min. Each sample was broken up using an MM400 mixer mill (Retsch) at a frequency of 30 Hz for 10 min, and the extracts were incubated at 4°C for 1 h. Then the extracts were centrifuged at 13,000 rpm for 10 min at 4°C, and the supernatants were collected. Supernatants were filtered using 0.2 μm polytetrafluoroethylene (PTFE) syringe filters (Chromdisc) and dried using a speed-vacuum concentrator (Biotron). The dried samples were processed in two steps of derivatization reaction before GC-TOF/MS analysis. Oximation was conducted first by adding 50 µL methoxyamine hydrochloride in pyridine (20 mg/mL) to the dried samples, and the reaction mixture was incubated at 30°C for 90 min. Subsequently, silylation was performed using adding 50 µL N-trimethylsilyl-N-methyl trifluoroacetamide (MSTFA) to the incubated reaction mixture, followed by incubation at 37°C for 30 min. All samples were filtered through a PTFE syringe filter before analysis. GC-TOF/MS analysis was performed using an Agilent 7890A system (Agilent Technologies) with an L-PAL3 autosampler and Pegasus III TOF-MS (Leco Corp.). The GC-TOF/MS raw data were obtained by MetaMass (Seoul, Korea) using LECO Chroma TOF™ software (version 4.44; Leco Corp.), and converted into the NetCDF format (*.cdf). Peak detection, peak intensity normalization, retention time evaluation, and alignment were performed using the Metalign software package (http://www.metalign.nl) and exported to an Excel file (Microsoft Corp.). Multivariate statistical analyses were performed using SIMCA-P+ (version 12.0; Umetrics). Partial least squares-discriminant analysis (PLS-DA) modeling was performed to compare the different metabolites between experimental groups. The significantly discriminant variables among experimental groups were selected based on a variable importance in projection (VIP) value >1.0 and tested for significance at p < 0.05. Inosine in mouse serum was detected by targeted metabolomics using GC-TOF/MS by MetaMass (Seoul, Korea) and confirmed based on the retention time and mass (m/z).
Related Knowledge Centers
- Gas Chromatography
- Mass Spectrometry
- Organosilicon Chemistry
- Trimethylsilyl Group
- Silyl Ether
- Trimethylsilyl Chloride
- Bis(Trimethylsilyl)Acetamide
- Silyl Enol Ether
- Tetra-N-Butylammonium Fluoride
- Coordination Complex