Molecular design aided by random forests and synthesis of potent trypanocidal agents as cruzain inhibitors for Chagas disease treatment

Abstract

Cruzain is an established target for the identification of novel trypanocidal agents, but how good are in vitro/in vivo correlations? This work describes the development of a random forests model for the prediction of the bioavailability of cruzain inhibitors that are Trypanosoma cruzi killers. Some common properties that characterize drug-likeness are poorly represented in many established cruzain inhibitors. This correlates with the evidence that many high-affinity cruzain inhibitors are not trypanocidal agents against T. cruzi. On the other hand, T. cruzi killers that present typical drug-like characteristics are likely to show better trypanocidal action than those without such features. The random forests model was not outperformed by other machine learning methods (such as artificial neural networks and support vector machines), and it was validated with the synthesis of two new trypanocidal agents. Specifically, we report a new lead compound, Neq0565, which was tested on T. cruzi Tulahuen (β-galactosidase) with a pEC₅₀ of 4.9. It is inactive in the host cell line showing a selectivity index (SI = EC₅₀^cyto /EC₅₀^{T. cruzi} ) higher than 50.

Keywords: Trypanosoma cruzi; Chagas disease; cruzain; machine learning; oral bioavailability; random forests.