Skip to content
2000
Volume 14, Issue 1
  • ISSN: 1573-4080
  • E-ISSN: 1875-6662

Abstract

Background: Depression associated with asthma enhances asthma-related morbidity and mortality. While antidepressants (AD) are prescribed in depression, β2-adrenergic agonists (BAA) and xanthines are used in asthma. Asthma treatment side effects include depression and suicidal ideation. Objective: Here, we identified a possible mechanism of BAA and xanthines to induce depressant or antidepressant responses by advanced methods of computational pharmacology correlated with clinical studies. Methods: Based on 18 AD, 7 BAA and two xanthines, here were generated quantitative structureactivity relationship (QSAR) models establishing the contribution of drugs molecular descriptors to the binding affinity for two membrane receptors deeply involved in depression, namely serotonin transporter SERT and serotonin receptor 5-HT1A. Results: QSAR models with good statistical validation parameters (q2 (cross-validated r2) higher than 0.70 and fitted correlation r2, higher than 0.80) were obtained considering the critical molecular descriptors: solvent accessible surface areas, energy of solvatation, hydrophobicity and count of rotatable bounds. The results suggest that: (i) clenbuterol should exert middle antidepressant effects, (ii) theophylline could induce suicidal ideation at the beginning of treatment due to its similarity with escitalopram, (iii) salbutamol may be safely used in depressive asthmatic patents, (iv) indacaterol antidepressant effects should be more significant than those of other long-acting BAA, and (v) the usage of fenoterol and formoterol may induce mania, delirium, etc. Conclusion: The molecular mechanisms of BAA and xanthines evaluated here represent important resources for future studies focused on depression in asthma.

Loading

Article metrics loading...

/content/journals/cei/10.2174/1573408013666170706102442
2018-04-01
2025-10-30
Loading full text...

Full text loading...

/content/journals/cei/10.2174/1573408013666170706102442
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test