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CoMFA analysis of dual/multiple PPAR activators.

Palak Shah1, Amit Mittal, Prasad V Bharatam

  • 1Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab 160062, India.

European Journal of Medicinal Chemistry
|March 7, 2008
PubMed
Summary
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Computational methods aid in designing dual activators, agents targeting multiple biological pathways for synergistic effects. The "additivity of molecular fields" concept showed success for PPARalpha/gamma but limitations for PPARdelta targets.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Dual or multiple activators offer synergistic therapeutic effects by engaging multiple biological targets.
  • Computational methods are valuable tools for designing such multi-target agents.
  • The 'additivity of molecular fields' concept provides a framework for designing dual activators.

Purpose of the Study:

  • To explore the scope and limitations of the 'additivity of molecular fields' concept in designing dual and multiple activators.
  • To evaluate the applicability of this concept for Peroxisome proliferator-activated receptors (PPARs).

Main Methods:

  • Development of comparative molecular field analysis (CoMFA) models.
  • Generation of individual, dual, and multiple PPAR activator models.

Related Experiment Videos

  • Application of the 'additivity of molecular fields' concept.

    • Main Results:

    • A successful dual PPARalpha/gamma CoMFA model was developed.
    • CoMFA models for dual PPARgamma/delta, dual PPARalpha/delta, and multiple PPARalpha/gamma/delta activators were less successful.
    • Poor correlation in the PPARdelta CoMFA model limited the development of broader models.

    Conclusions:

    • The 'additivity of molecular fields' concept is effective for designing dual PPARalpha/gamma activators.
    • The concept's applicability is constrained by the predictive quality of individual target models, particularly PPARdelta.
    • Further refinement is needed to extend this computational approach to more complex multi-target drug design scenarios.