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Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage.

ABSTRACT: Chagas disease affects 8-11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomena have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival and immune evasion. In this work, we compiled and curated a database of diverse cruzain inhibitors previously reported in the literature. From this data set, quantitative structure-activity relationship (QSAR) models for the prediction of their pIC₅₀ values were generated using k-nearest neighbors and random forest algorithms. Local and global models were calculated and compared. The statistical parameters for internal and external validation indicate a significant predictability, with q _loo ² values around 0.66 and 0.61 and external R ² coefficients of 0.725 and 0.766. The applicability domain is quantitatively defined, according to QSAR good practices, using the leverage and similarity methods. The models described in this work are readily available in a Python script for the discovery of novel cruzain inhibitors.

SUBMITTER: Rosas-Jimenez JG

PROVIDER: S-EPMC7970485 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage.

Rosas-Jimenez Jose Guadalupe JG Garcia-Revilla Marco A MA Madariaga-Mazon Abraham A Martinez-Mayorga Karina K

ACS omega 20210305 10

Chagas disease affects 8-11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomena have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival ...[more]

PMID: 33748586

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Project description: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.

Project description:Cytotoxicity is a critical property in determining the fate of a small molecule in the drug discovery pipeline. Cytotoxic compounds are identified and triaged in both target-based and cell-based phenotypic approaches due to their off-target toxicity or on-target and on-mechanism toxicity for oncology and neurodegenerative targets. It is critical that chemical-induced cytotoxicity be reliably predicted before drug candidates advance to the late stage of development, or more ideally, before compounds are synthesized. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in NCATS annotated libraries against four 'normal' cell lines (HEK 293, NIH 3T3, CRL-7250 and HaCat) using CellTiter-Glo (CTG) technology and constructed highly predictive models to estimate cytotoxicity from chemical structures. There are 5,241 non-redundant compounds having unambiguous activities in the four different cell lines, among which 11.8% compounds exhibited cytotoxicity in two or more cell lines and are thus labelled cytotoxic. The support vector classification (SVC) models trained with 80% randomly selected molecules achieved the area under the receiver operating characteristic curve (AUC-ROC) of 0.88 on average for the remaining 20% compounds in the test sets in 10 repeating experiments. Application of under-sampling rebalancing method further improved the averaged AUC-ROC to 0.90. Analysis of structural features shared by cytotoxic compounds may offer medicinal chemists heuristic design ideas to eliminate undesirable cytotoxicity. The profiling of cytotoxicity of drug-like molecules with annotated primary mechanism of action (MOA) will inform on the roles played by different targets or pathways in cellular viability. The predictive models for cytotoxicity (accessible at https://tripod.nih.gov/web_adme/cytotox.html) provide the scientific community a fast yet reliable way to prioritize molecules with little or no cytotoxicity for downstream development.

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Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage.

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Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage.

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