Cascade Ligand- and Structure-Based Virtual Screening to Identify New Trypanocidal Compounds Inhibiting Putrescine Uptake

Abstract

Chagas disease is a neglected tropical disease endemic to Latin America, though migratory movements have recently spread it to other regions. Here, we have applied a cascade virtual screening campaign combining ligand- and structure-based methods. In order to find novel inhibitors of putrescine uptake in Trypanosoma cruzi, an ensemble of linear ligand-based classifiers obtained by has been applied as initial screening filter, followed by docking into a homology model of the putrescine permease TcPAT12. 1,000 individual linear classifiers were inferred from a balanced dataset. Subsequently, different schemes were tested to combine the individual classifiers: MIN operator, average ranking, average score, average voting, with MIN operator leading to the best performance. The homology model was based on the arginine/agmatine antiporter (AdiC) from Escherichia coli as template. It showed 64% coverage of the entire query sequence and it was selected based on the normalized Discrete Optimized Protein Energy parameter and the GA341 score. The modeled structure had 96% in the allowed area of Ramachandran's plot, and none of the residues located in non-allowed regions were involved in the active site of the transporter. Positivity Predictive Value surfaces were applied to optimize the score thresholds to be used in the ligand-based virtual screening step: for that purpose Positivity Predictive Value was charted as a function of putative yields of active in the range 0.001–0.010 and the Se/Sp ratio. With a focus on drug repositioning opportunities, DrugBank and Sweetlead databases were subjected to screening. Among 8 hits, cinnarizine, a drug frequently prescribed for motion sickness and balance disorder, was tested against T. cruzi epimastigotes and amastigotes, confirming its trypanocidal effects and its inhibitory effects on putrescine uptake. Furthermore, clofazimine, an antibiotic with already proven trypanocidal effects, also displayed inhibitory effects on putrescine uptake. Two other hits, meclizine and butoconazole, also displayed trypanocidal effects (in the case of meclizine, against both epimastigotes and amastigotes), without inhibiting putrescine uptake.

Machine Learning in Drug Discovery and Development Part 1: A Primer

“…If these “false positive” covariates are then included in model, this will contribute to overfitting. We have observed that the use of small random subsets of descriptors (a “random subspace” approximation) is a useful strategy to mitigate the chance of spurious correlations . Ensemble learning, which is the combination of individuals models into a meta‐model (e.g., random forest), can also improve model robustness.…”

“…This example is an adaptation of the work of Alberca et al to obtain a ML model for the subsequent search for drugs against T. cruzi , i.e., putrescine uptake inhibitors. T. cruzi is a parasite that is transmitted to animals and people by insect vectors and causes Chagas disease, a neglected tropical infectious disease endemic to Latin America .…”

“…T. cruzi is a parasite that is transmitted to animals and people by insect vectors and causes Chagas disease, a neglected tropical infectious disease endemic to Latin America . Putrescine is a low‐molecular‐weight polyamine with crucial importance for the parasite survival . Different from humans, T. cruzi cannot synthesize putrescine and must uptake it from the human host via a high‐affinity putrescine transporter; this makes putrescine uptake an attractive target for drugs against T. cruzi …”

“…Alberca et al . developed a linear regression model using small random subsets of descriptors (a random subspace approximation) followed by an ensemble‐learning procedure to combine the results ( Supplementary Material ) .…”

“…Alberca et al . developed a linear regression model using small random subsets of descriptors (a random subspace approximation) followed by an ensemble‐learning procedure to combine the results ( Supplementary Material ) . Although we follow the data set preprocessing (i.e., creation of the training and validation data sets) of Alberca et al ., a random forest model and a LASSO model are developed for demonstration ( Supplementary Material ).…”