Structure-activity relationships of synthetic cardiac glycosides

Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of Na+/K+-ATPase

BACKGROUND

Quantitative structure-activity relationship (QSAR) modeling is a method of characterizing the relationship between chemical structures and biological activity. Automated machine learning enables computers to learn from large datasets and can be used for chemoinformatics. Cardiac steroids (CSs) inhibit the activity of Na⁺/K⁺-ATPase (NKA) in several species, including humans, since the binding pocket in which NKA binds to CSs is highly conserved. CSs are used to treat heart disease and have been developed into anticancer drugs for use in clinical trials. Novel CSs are, therefore, frequently synthesized and their activities evaluated. The purpose of this study is to develop a QSAR model via automated machine learning to predict the potential inhibitory activity of compounds without performing experiments.

METHODS

The chemical structures and inhibitory activities of 215 CS derivatives were obtained from the scientific literature. Predictive QSAR models were constructed using molecular descriptors, fingerprints, and biological activities.

RESULTS

The best predictive QSAR models were selected based on the LogLoss value. Using these models, the Matthews correlation coefficient, F1 score, and area under the curve of the test dataset were 0.6729, 0.8813, and 0.8812, respectively. Next, we showed automated construction of the predictive models for CS derivatives, which may be useful for identifying novel CSs suitable for candidate drug development.

CONCLUSION

The automated machine learning-based QSAR method developed here should be applicable for the time-efficient construction of predictive models using only a small number of compounds.

Identification of novel antipoxviral agents: mitoxantrone inhibits vaccinia virus replication by blocking virion assembly

The bioterror threat of a smallpox outbreak in an unvaccinated population has mobilized efforts to develop new antipoxviral agents. By screening a library of known drugs, we identified 13 compounds that inhibited vaccinia virus replication at noncytotoxic doses. The anticancer drug mitoxantrone is unique among the inhibitors identified in that it has no apparent impact on viral gene expression. Rather, it blocks processing of viral structural proteins and assembly of mature progeny virions. The isolation of mitoxantrone-resistant vaccinia strains underscores that a viral protein is the likely target of the drug. Whole-genome sequencing of mitoxantrone-resistant viruses pinpointed missense mutations in the N-terminal domain of vaccinia DNA ligase. Despite its favorable activity in cell culture, mitoxantrone administered intraperitoneally at the maximum tolerated dose failed to protect mice against a lethal intranasal infection with vaccinia virus.

Biological activities of synthetic triterpenoid and steroid beta-D-xylopyranosyl-(1-->6)-beta-D-glucopyranosides

Comparisons of the activities of diosgenyl, methyl glycyrrhetinate or digitoxigenyl 3-O-beta-D-xylopyranosyl-(1-->6)-beta-D-glucopyranoside with those of our previous glycosides supported our assumptions that both haemolytic and antifungal activities of steroid saponins are generally parallel to each other, while almost all haemolytic triterpenoid saponins show no antifungal activity, and that both antiviral and anti-ATPase activities of cardiac glycosides having a (1-->6) sugar linkage are much lower than those of the others.