Articles & Third-Party References

Recent journal articles and third-party references about ACD/Labs from the last year

A simple approach to multifunctionalized N1-alkylated 7-amino-6-azaoxindole derivatives using their in situ stabilized tautomer form
Oct 13, 2016
N.T. Tzvetkov, B. Neumann, H. Stammler, L. Antonov
Third-Party Reference
A simple approach for the synthesis of multifunctionalized N1-alkyl 7-amino-6-azaoxindole derivatives was developed and investigated. Formation of 5-amino- and 7-amino-6-aza-2-oxindoles 12a and 13a, respectively, was achieved using an intramolecular reductive cyclization as a key step. Subsequent alkylation of the pyrrole N1 atom in 12a led to the desired N1-alkylated compounds 22a24 comprising different functionalities. Alkylation of 5-amino-substituted regioisomer 13a under the same conditions as used for 12a did not resulted in N1-alkylated products. To find a plausible explanation for the observed differences in reactivity, we investigated the possible tautomers of 12a and 13a and the distribution of their neutral and ionized forms in a gas phase. The relevant physicochemical properties of compounds 12a and 23 were determined.
Tetrahedron, 72(41): 6455-66, 2016.

Prediction Models of Retention Indices for Increased Confidence in Structural Elucidation during Complex Matrix Analysis: Application to Gas Chromatography Coupled with High-Resolution Mass Spectrometry
Aug 02, 2016
Dossin E, Martin E, Diana P, Castellon A, Monge A, Pospisil P, Bentley M, Guy PA
Third-Party Reference
Analytical Chemistry
Monitoring of volatile and semivolatile compounds was performed using gas chromatography (GC) coupled to high-resolution electron ionization mass spectrometry, using both headspace and liquid injection modes. A total of 560 reference compounds, including 8 odd n-alkanes, were analyzed and experimental linear retention indices (LRI) were determined. These reference compounds were randomly split into training (n = 401) and test (n = 151) sets. LRI for all 552 reference compounds were also calculated based upon computational Quantitative Structure-Property Relationship (QSPR) models, using two independent approaches RapidMiner (coupled to Dragon) and ACD/ChromGenius software. Correlation coefficients for experimental versus predicted LRI values calculated for both training and test set compounds were calculated at 0.966 and 0.949 for RapidMiner and at 0.977 and 0.976 for ACD/ChromGenius, respectively. In addition, the cross-validation correlation was calculated at 0.96 from RapidMiner and the residual standard error value obtained from ACD/ChromGenius was 53.635. These models were then used to predict LRI values for several thousand compounds reported present in tobacco and tobacco-related fractions, plus a range of specific flavor compounds. It was demonstrated that using the mean of the LRI values predicted by RapidMiner and ACD/ChromGenius, in combination with accurate mass data, could enhance the confidence level for compound identification from the analysis of complex matrixes, particularly when the two predicted LRI values for a compound were in close agreement. Application of this LRI modeling approach to matrixes with unknown composition has already enabled the confirmation of 23 postulated compounds, demonstrating its ability to facilitate compound identification in an analytical workflow. The goal is to reduce the list of putative candidates to a reasonable relevant number that can be obtained and measured for confirmation.
Dossin E, et al.. (2016). Prediction Models of Retention Indices for Increased Confidence in Structural Elucidation during Complex Matrix Analysis: Application to Gas Chromatography Coupled with High-Resolution Mass Spectrometry. Anal. Chem., 88(15): 7539-47.

Complex LC Method Development using Method Development System and LC Simulation Software
Jun 22, 2016
K. Zhu, M. Pursch, B. Gu
Third-Party Reference
Jun 19, 2016, HPLC
View Poster

Database extraction of metabolite information of drug candidates: Analysis of 27 AstraZeneca compounds with human ADME data
Feb 11, 2016
J. Iegre, M.A. Hayes, R.A. Thompson, L. Weidolf, and E.M. Isin
Third-Party Reference
Drug Metabolism & Disposition
As part of the drug discovery and development process, it is important to understand the human metabolism of a candidate drug prior to clinical studies. Pre-clinical in vitro and in vivo experiments across species are conducted to build knowledge concerning human circulating metabolites in preparation for clinical studies and therefore, the quality of these experiments is critical. Within AstraZeneca, all metabolite identification (Met-ID) information is stored in a global database using ACDLabs software. In this study, the Met-ID information derived from in vitro and in vivo studies for 27 AstraZeneca drug candidates that underwent human ADME studies was extracted from the database. The retrospective analysis showed that 81% of human circulating metabolites were previously observed in pre-clinical in vitro and/or in vivo experiments. A detailed analysis was carried out to understand which human circulating metabolites were not captured in the pre-clinical experiments. Metabolites observed in human hepatocytes and rat plasma, but not seen in circulation in humans (extraneous metabolites) were also investigated. The majority of human specific circulating metabolites derive from multi-step biotransformation reactions that may not be observed in in vitro studies within the limited time frame cryopreserved hepatocytes are active. Factors leading to the formation of extraneous metabolites in pre-clinical studies seemed to be related to species differences with respect to transporter activity, secondary metabolism and enzyme kinetics. This retrospective analysis assesses the predictive value of Met-ID experiments and improves our ability to discriminate between metabolites expected to circulate in humans and irrelevant metabolites seen in pre-clinical studies.
Drug Metab Dispos. published online Feb. 11, 2016

Computer-Assisted Structure Elucidation in Routine Analysis
Jan 26, 2016
P. Wheeler, S. Hayward, M. Elyashberg
American Laboratory
The elucidation of unknown structures, especially those with novel moieties found in natural products, often results in initially incorrect published structures, which then require either exhaustive spectroscopic analysis, full chemical synthesis or both to prove the correct structure. In many cases, the initial incorrect structure and subsequent analytical work could be avoided using computer-assisted structure elucidation (CASE).
P. Wheeler, S. Hayward, M. Elyashberg. (2016). Computer-Assisted Structure Elucidation in Routine Analysis. American Laboratory, 48(2): 1214.

To view all of our past articles and third-party references, please use our resource search.