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Mariam Bonyadi Camacho and Thomas J Anastasio
University of Illinois at Urbana-Champaign, USA
Posters & Accepted Abstracts: Drug Des
Statement of the Problem: Major depressive disorder is prevalent and debilitating and current pharmacological interventions are far from effective. The clinically observed heterogeneity in antidepressant response is not well understood. Methodology: We present a computational model that represents the antidepressant response to predict novel drug combinations with potentially greater efficacy than currently available treatment options. The model includes the interactions between the monoamine producing brain regions and three non-monoaminergic neurotransmitter systems and simulates homeostatic adaptation to chronic antidepressant administration by adjusting the strengths of eleven receptors that are known to adjust under chronic antidepressant. The model has many ways to adapt to chronic antidepressant and different adapted states are associated with different levels of monoamine production. Findings: In terms of the percentage of adapted states with therapeutically elevated monoamines, our model agrees closely with the clinically-observed efficacies of 12 antidepressant drugs and combinations, including the low efficacy of selective serotonin reuptake inhibitors (SSRIs; clinical efficacy 25-50%, model 29%). The model also predicts that augmenting SSRIs with Pexacerfont (Pex, a corticotropin releasing factor-1 receptor antagonist), can enhance therapeutic efficacy over SSRI alone by both increasing the percentage of adapted configurations with therapeutic serotonin levels (from 29% with SSRI to 31% with SSRI/Pex) and with elevated dopamine levels (from 0% SSRI to 100% SSRI/Pex). Conclusion & Significance: The model provides a potential explanation for the heterogeneity in antidepressant response, that the brain can reach similar levels of adaptation in many ways, but not all adapted configurations are associated with therapeutic monoamine levels.