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Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation

Current hypotheses cannot fully explain the clinically observed heterogeneity in antidepressant response. The therapeutic latency of antidepressants suggests that therapeutic outcomes are achieved not by the acute effects of the drugs, but rather by the homeostatic changes that occur as the brain ad...

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Autores principales: Camacho, Mariam B., Anastasio, Thomas J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5770730/
https://www.ncbi.nlm.nih.gov/pubmed/29375372
http://dx.doi.org/10.3389/fphar.2017.00925
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author Camacho, Mariam B.
Anastasio, Thomas J.
author_facet Camacho, Mariam B.
Anastasio, Thomas J.
author_sort Camacho, Mariam B.
collection PubMed
description Current hypotheses cannot fully explain the clinically observed heterogeneity in antidepressant response. The therapeutic latency of antidepressants suggests that therapeutic outcomes are achieved not by the acute effects of the drugs, but rather by the homeostatic changes that occur as the brain adapts to their chronic administration. We present a computational model that represents the known interactions between the monoaminergic neurotransmitter-producing brain regions and associated non-monoaminergic neurotransmitter systems, and use the model to explore the possible ways in which the brain can homeostatically adjust to chronic antidepressant administration. The model also represents the neuron-specific neurotransmitter receptors that are known to adjust their strengths (expressions or sensitivities) in response to chronic antidepressant administration, and neuroadaptation in the model occurs through sequential adjustments in these receptor strengths. The main result is that the model can reach similar levels of adaptation to chronic administration of the same antidepressant drug or combination along many different pathways, arriving correspondingly at many different receptor strength configurations, but not all of those adapted configurations are also associated with therapeutic elevations in monoamine levels. When expressed as the percentage of adapted configurations that are also associated with elevations in one or more of the monoamines, our modeling results largely agree with the percentage efficacy rates of antidepressants and antidepressant combinations observed in clinical trials. Our neuroadaptation model provides an explanation for the clinical reports of heterogeneous outcomes among patients chronically administered the same antidepressant drug regimen.
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spelling pubmed-57707302018-01-26 Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation Camacho, Mariam B. Anastasio, Thomas J. Front Pharmacol Pharmacology Current hypotheses cannot fully explain the clinically observed heterogeneity in antidepressant response. The therapeutic latency of antidepressants suggests that therapeutic outcomes are achieved not by the acute effects of the drugs, but rather by the homeostatic changes that occur as the brain adapts to their chronic administration. We present a computational model that represents the known interactions between the monoaminergic neurotransmitter-producing brain regions and associated non-monoaminergic neurotransmitter systems, and use the model to explore the possible ways in which the brain can homeostatically adjust to chronic antidepressant administration. The model also represents the neuron-specific neurotransmitter receptors that are known to adjust their strengths (expressions or sensitivities) in response to chronic antidepressant administration, and neuroadaptation in the model occurs through sequential adjustments in these receptor strengths. The main result is that the model can reach similar levels of adaptation to chronic administration of the same antidepressant drug or combination along many different pathways, arriving correspondingly at many different receptor strength configurations, but not all of those adapted configurations are also associated with therapeutic elevations in monoamine levels. When expressed as the percentage of adapted configurations that are also associated with elevations in one or more of the monoamines, our modeling results largely agree with the percentage efficacy rates of antidepressants and antidepressant combinations observed in clinical trials. Our neuroadaptation model provides an explanation for the clinical reports of heterogeneous outcomes among patients chronically administered the same antidepressant drug regimen. Frontiers Media S.A. 2017-12-20 /pmc/articles/PMC5770730/ /pubmed/29375372 http://dx.doi.org/10.3389/fphar.2017.00925 Text en Copyright © 2017 Camacho and Anastasio. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Camacho, Mariam B.
Anastasio, Thomas J.
Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation
title Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation
title_full Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation
title_fullStr Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation
title_full_unstemmed Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation
title_short Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation
title_sort computational model of antidepressant response heterogeneity as multi-pathway neuroadaptation
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5770730/
https://www.ncbi.nlm.nih.gov/pubmed/29375372
http://dx.doi.org/10.3389/fphar.2017.00925
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