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Common SAR Derived from Multiple QSAR Models on Vorinostat Derivatives Targeting HDACs in Tumor Treatment

Sugathan Praseetha, Srinivas Bandaru, Mukesh Yadav

  • 1Research and Development Centre, Bharathiyar University, Coimbatore, 641046, India. suresh@kufos.ac.in.

Current Pharmaceutical Design
|June 25, 2016
PubMed
Abstract

Insights

Histone deacetylase (HDAC) inhibitors are promising cancer therapeutics. This study developed quantitative structure-activity relationship (QSAR) models for Vorinostat analogues, identifying key molecular descriptors for potent HDAC inhibition.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Dysregulation of histone deacetylases (HDACs) is linked to cancer development.
  • HDAC inhibition presents a promising therapeutic strategy for malignancies.

Purpose of the Study:

  • To investigate the structure-activity relationship (SAR) of Vorinostat analogues.
  • To develop predictive quantitative structure-activity relationship (QSAR) models for HDAC inhibition.

Main Methods:

  • Synthesis and evaluation of Vorinostat analogues for biological activity.
  • Development of multiple linear regression (MLR) and support vector machine (SVM) QSAR models.
  • Utilized 3-D Morse and Radial Distribution Function indices as molecular descriptors.

Main Results:

  • Statistically significant and predictive QSAR models were developed for HDAC1 and HDAC8 inhibition.
  • High statistical fit and stability were achieved for both linear and non-linear models.
  • R-squared values for HDAC1 models: linear (0.8089), non-linear (0.9801).
  • R-squared values for HDAC8 models: linear (0.9454), non-linear (0.9899).

Conclusions:

  • Molecular descriptors from 3-D Morse and Radial Distribution Function indices were selective and crucial for modeling HDAC inhibition.
  • These descriptors capture common SAR among Vorinostat derivatives, indicating their potential for potent HDAC inhibition.

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