Application of clustering algorithms and pharmacophore screening for identification of thiazolidinone and pyrazoline derivatives with dual antiparasitic and anticancer activity

Authors

DOI:

https://doi.org/10.15587/2519-4852.2025.328968

Keywords:

machine learning, thiazolidinone, pyrazoline, antitrypanosomal acivity, anticancer activity, pharmacophore design

Abstract

Thiazolidinones and related heterocycles exhibiting antimicrobial, antiparasitic, anticancer, antidiabetic, and anti-inflammatory activities are considered privileged scaffolds for the development of novel, drug-like molecules. 4-Thiazolidinone-based hybrids with alkanecarboxylic acid moieties, pyrazoline, phenylindole or imidazothiadiazole fragments have been thoroughly investigated as potential antiparasitic agents. Along with numerous studies that proved their high anticancer potential, this class of compounds is attractive for the promising strategy of redirecting antiparasitic drugs for cancer treatment.

The aim of the study. We aimed to investigate the correlation between the antileukemic and antiparasitic properties of various thiazolidinone and pyrazoline derivatives.

Materials and methods. The anticancer activity of a data set of 31 compounds against five Leukemic cell lines was studied at a single concentration (10−5M). The antitrypanosomal activity data has been collected under the same assay protocol against Trypanosoma brucei brucei (Tbb). The clustering algorithms were implemented in Python using the NumPy, Pandas, Scikit-learn, Matplotlib, and Plotly libraries. LigandScout 4.4 software was used for the 3D-pharmacophore design.

Results. The compounds with antitrypanosomal activity were divided into 3 classes according to the IC50 values calculated in the growth inhibition assay against Tbb. The percentage of cell growth in the in vitro assay of studied compounds on five Leukemic cell lines was used for the machine learning study. Applying both the K-means and Agglomerative hierarchical clustering algorithms, compounds from class 1 were grouped into one cluster. The pharmacophore screening using merged pharmacophore derived from BCL-2-venetoclax complexes showed good pharmacophore-fit scores for the compounds selected in one cluster by both algorithms. The same pharmacophore model, when applied to a dataset of thiazolidinone/thiazole-indole/imidazothiadiazole hybrid molecules with high antitrypanosomal activity in vitro, assigned them as active.

Conclusions. The findings of the study suggest that thiazolidine derivatives and related compounds exhibit dual anti-parasitic and anticancer properties, which may help to identify their antiproliferative mechanism of action in parasitic and cancer cells.

Author Biographies

Anna Kryshchyshyn-Dylevych, Danylo Halytsky Lviv National Medical University

Doctor of Pharmaceutical Sciences, Professor

Department of Pharmaceutical, Organic and Bioorganic Chemistry

Roman Lesyk, Danylo Halytsky Lviv National Medical University

Doctor of Pharmaceutical Sciences, Professor, Head of Department

Department of Pharmaceutical, Organic and Bioorganic Chemistry

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Application of clustering algorithms and pharmacophore screening for identification of thiazolidinone and pyrazoline derivatives with dual antiparasitic and anticancer activity

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Published

2025-06-30

How to Cite

Kryshchyshyn-Dylevych, A., & Lesyk, R. (2025). Application of clustering algorithms and pharmacophore screening for identification of thiazolidinone and pyrazoline derivatives with dual antiparasitic and anticancer activity. ScienceRise: Pharmaceutical Science, (3 (55), 17–29. https://doi.org/10.15587/2519-4852.2025.328968

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No. 3 (55) (2025)

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Pharmaceutical Science

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