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Predicting antitrichomonal activity: a computational screening using atom-based bilinear indices and experimental

Yovani Marrero-Ponce1, Alfredo Meneses-Marcel, Juan A Castillo-Garit

  • 1Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, Poligon la Coma s/n (detras de Canal Nou), PO Box 22085, E-46071 Valencia, Spain. ymarrero77@yahoo.es

Bioorganic & Medicinal Chemistry
|August 1, 2006
PubMed
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New computational models identified novel antitrichomonal agents for treating trichomoniasis. These Quantitative Structure-Activity Relationship (QSAR) models predict potent and less toxic compounds, addressing limitations of current therapies and drug resistance.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Parasitology

Background:

  • Current therapies for Trichomonas vaginalis infections are often inaccessible, toxic, or costly, particularly in developing nations.
  • Emerging resistance to metronidazole and the toxicity of existing drugs necessitate the development of novel antitrichomonal agents.
  • There is a critical need for safer and more effective treatments for trichomoniasis, especially for vulnerable populations like pregnant women.

Purpose of the Study:

  • To discover novel, potent, and non-toxic lead trichomonacidal compounds using computational methods.
  • To develop robust Quantitative Structure-Activity Relationship (QSAR) models for predicting antitrichomonal activity.
  • To identify new chemical entities with unique structural features for further optimization against Trichomonas vaginalis.

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Main Methods:

  • Utilized atom-based bilinear indices (TOMOCOMD-CARDD descriptor) and linear discriminant analysis (LDA) for QSAR modeling.
  • Calculated atomic-level molecular descriptors based on atomic masses, van der Waals volumes, polarizabilities, and electronegativities.
  • Employed Randić orthogonalization to refine QSAR models and computationally screened compounds for antitrichomonal activity.

Main Results:

  • Developed LDA-based QSAR models with high classification accuracy (up to 94.51% training, 93.75% test) and significant correlation coefficients.
  • Successfully identified six potential lead compounds through computational screening.
  • Synthesized compounds demonstrated in vitro activity, with three showing high to moderate cytocidal effects and two exhibiting cytostatic activity against T. vaginalis.

Conclusions:

  • The developed LDA-based QSAR models are robust and predictive, offering a valuable computer-assisted system for drug discovery.
  • The identified compounds, possessing novel structural features, represent promising leads for developing next-generation antitrichomonal therapies.
  • This approach can significantly reduce the experimental burden in discovering new chemical entities against Trichomonas vaginalis.