Fluorinated steroids were examined using 1D and 2D homo- and heteronuclear (19)F NMR, such as (19)F-(1) H and (19)F-(13)C. The utilization of fluorine NMR accounted for spectral simplification and resulted in a straightforward pathway for the determination of structures including the configuration of these compounds; these steroids present an illustrative example for other types of fluorinated compounds, which are increasingly encountered in drug discovery. The potential of (19)F NMR is elaborated on in detail for two compounds containing diastereotopic fluorines with different coupling patterns. The analysis of the coupling patterns and the through-space interactions resulted in the determination of the structure and configuration. Heteronuclear correlation experiments, i.e. (19)F-(1)H HETCOR, (19)F-(13)C HMQC and HMBC, and (19)F-(1)H HOESY, were applied to determine first the relative stereochemistry and then the molecular configuration at C4 and C5 of a steroidal compound bearing a fused three-membered ring with two fluorine substituents. These examples proved (19)F NMR to be a useful addition to the extensively used (1)H and (13)C NMR within structure elucidation and configuration determination of small molecules.
Indirect and unsymmetrical indirect covariance NMR provide powerful tools to compute and visualize correlation information by transforming component spectra into combined spectral data matrices. Sensitive component spectra such as TOCSY, HSQC and NOESY can be quickly converted into experimentally insensitive or time-consuming correlation spectra such as HSQC-NOESY. The comparison of illustrative series of spectra from four steroids, dexamethasone, testosterone, allylestrenol and tibolone, renders the effects of resonance overlap on the ease of interpretation visible. The compounds are selected such that signal overlap increases systematically in the proton and carbon domain. Spectra are defined as light, moderate and heavy signal overlap, based on signal density. The investigation suggests that moderate spectral congestion in either proton or carbon domain leads to a number of artifacts that does not hamper signal assignment but lowers the level of confidence on de novo structure elucidation. Since the number of correlations usually increases through covariance processing, component spectra with severe spectral congestion in both dimensions are not suitable for covariance processing and the resulting spectra do not support structure confirmation or structure elucidation. The calculated spectra are compared with the corresponding experimental spectra with respect to their application in structure elucidation laboratory environments.
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