Optimization of a pharmacophore model for 5-HT4 agonists using CoMFA and receptor based alignment

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• 5-HT4 receptors are crucial targets for various therapeutic applications.
• Developing selective and potent 5-HT4 agonists requires robust predictive models.
• Existing structure-activity relationship (SAR) studies provide a foundation for computational modeling.

Purpose of the Study:

• To develop and compare two Comparative Molecular Field Analysis (CoMFA) models for predicting 5-HT4 agonist activity.
• To identify key molecular features contributing to 5-HT4 agonist potency.
• To guide the design of novel, more effective 5-HT4 agonists.

Main Methods:

• Utilized a dataset of 22 known and synthesized 5-HT4 agonists.
• Developed two CoMFA models: Model A (atom overlapping alignment) and Model B (alignment incorporating receptor binding site interactions).
• Evaluated and compared model predictivity using q2 values and analyzed steric and electrostatic contributions.

Main Results:

• Model B demonstrated slightly superior predictive ability (q2 = 0.582) compared to Model A (q2 = 0.564).
• Model B's predictive power was primarily driven by electrostatic contributions (0.664), whereas Model A relied more on steric factors (0.502).
• Steric contributions were significantly lower in Model B (0.270) compared to Model A (0.502).
• LogP contributions were minimal in both models (0.085).
• Synthesized compounds exhibited agonist activity at the micromolar (μmol) level.

Conclusions:

• CoMFA modeling is effective for predicting 5-HT4 agonist activity.
• Incorporating specific receptor binding site interactions into the alignment process (Model B) enhances predictive accuracy.
• Electrostatic interactions are dominant features for 5-HT4 agonist activity in the developed models.
• The synthesized compounds represent a promising starting point for further drug development targeting 5-HT4 receptors.