Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage

The pursuit of accurate predictive models of the bioactivity of small molecules

Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.

Targeting Cysteine Proteases and their Inhibitors to Combat Trypanosomiasis

BACKGROUND

Trypanosomiasis, caused by protozoan parasites of the Trypanosoma genus, remains a significant health burden in several regions of the world. Cysteine proteases play a crucial role in the pathogenesis of Trypanosoma parasites and have emerged as potential therapeutic targets for the development of novel antiparasitic drugs.

INTRODUCTION

This review article aims to provide a comprehensive overview of the role of cysteine proteases in trypanosomiasis and their potential as therapeutic targets. We discuss the biological significance of cysteine proteases in Trypanosoma parasites and their involvement in essential processes, such as host immune evasion, cell invasion, and nutrient acquisition.

METHODS

A comprehensive literature search was conducted to identify relevant studies and research articles on the role of cysteine proteases and their inhibitors in trypanosomiasis. The selected studies were critically analyzed to extract key findings and provide a comprehensive overview of the topic.

RESULTS

Cysteine proteases, such as cruzipain, TbCatB and TbCatL, have been identified as promising therapeutic targets due to their essential roles in Trypanosoma pathogenesis. Several small molecule inhibitors and peptidomimetics have been developed to target these proteases and have shown promising activity in preclinical studies.

CONCLUSION

Targeting cysteine proteases and their inhibitors holds great potential for the development of novel antiparasitic drugs against trypanosomiasis. The identification of potent and selective cysteine protease inhibitors could significantly contribute to the combat against trypanosomiasis and improve the prospects for the treatment of this neglected tropical disease.

Convergent QSAR Models for the Prediction of Cruzain Inhibitors

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

Molecular targets for Chagas disease: validation, challenges and lead compounds for widely exploited targets

INTRODUCTION

Chagas disease (CD) imposes social and economic burdens, yet the available treatments have limited efficacy in the disease's chronic phase and cause serious adverse effects. To address this challenge, target-based approaches are a possible strategy to develop new, safe, and active treatments for both phases of the disease.

AREAS COVERED

This review delves into target-based approaches applied to CD drug discovery, emphasizing the studies from the last five years. We highlight the proteins cruzain (CZ), trypanothione reductase (TR), sterol 14 α-demethylase (CPY51), iron superoxide dismutase (Fe-SOD), proteasome, cytochrome b (Cytb), and cleavage and polyadenylation specificity factor 3 (CPSF3), chosen based on their biological and chemical validation as drug targets. For each, we discuss its biological relevance and validation as a target, currently related challenges, and the status of the most promising inhibitors.

EXPERT OPINION

Target-based approaches toward developing potential CD therapeutics have yielded promising leads in recent years. We expect a significant advance in this field in the next decade, fueled by the new options for Trypanosoma cruzi genetic manipulation that arose in the past decade, combined with recent advances in computational chemistry and chemical biology.

Design, synthesis and biological evaluation of novel thiosemicarbazones as cruzipain inhibitors

Based on the activity of 23 TSCs on CZ taken from the literature, we have developed a QSAR model for predicting the activity of TSCs. New TSCs were designed and then tested against CZP, resulting in inhibitors with IC₅₀ values in the nanomolar range. The modelling of the corresponding TSC-CZ complexes by molecular docking and QM/QM ONIOM refinement indicates a binding mode compatible with what was expected for active TSCs, according to a geometry-based theoretical model previously developed by our research group. Kinetic experiments on CZP suggest that the new TSCs act by a mechanism that involves the formation of a reversible covalent adduct with slow association and dissociation kinetics. These results demonstrate the strong inhibitory effect of the new TSCs and the benefit of the combined use of QSAR and molecular modelling techniques in the design of new and potent CZ/CZP inhibitors.

Computational approaches towards the discovery and optimisation of cruzain inhibitors

The need to develop safer and more efficacious drugs to treat Chagas disease has motivated the search for cruzain inhibitors. Cruzain is the recombinant, truncated version of cruzipain, a cysteine protease from Trypanosoma cruzi with important roles during the parasite life cycle. Several computational techniques have been applied to discover and optimise cruzain inhibitors, providing a molecular basis to guide this process. Here, we review some of the most recent computational studies that provided important information for the design of cruzain inhibitors. Moreover, we highlight the diversity of applications of in silico techniques and their impact.

Chemoinformatics Studies on a Series of Imidazoles as Cruzain Inhibitors

Natural products based on imidazole scaffolds have inspired the discovery of a wide variety of bioactive compounds. Herein, a series of imidazoles that act as competitive and potent cruzain inhibitors was investigated using a combination of ligand- and structure-based drug design strategies. Quantitative structure-activity relationships (QSARs) were generated along with the investigation of enzyme-inhibitor molecular interactions. Predictive hologram QSAR (HQSAR, r²_pred = 0.80) and AutoQSAR (q² = 0.90) models were built, and key structural properties that underpin cruzain inhibition were identified. Moreover, comparative molecular field analysis (CoMFA, r²_pred = 0.81) and comparative molecular similarity indices analysis (CoMSIA, r²_pred = 0.73) revealed 3D molecular features that strongly affect the activity of the inhibitors. These findings were examined along with molecular docking studies and were highly compatible with the intermolecular contacts that take place between cruzain and the inhibitors. The results gathered herein revealed the main factors that determine the activity of the imidazoles studied and provide novel knowledge for the design of improved cruzain inhibitors.