There are no reliable screening tools that predict clinical outcome in the preclinical stages of antidepressant drug development, this is an analytical shortcoming that has led to a recent decline in resources for antidepressant drug discovery. Given that by 2030 depression is predicted to become one of the leading global disease burdens, it is particularly pertinent to create analytical tools that accurately screen the efficacy of antidepressant therapies. The long-term goal of the proposed research is to understand how antidepressants modulate the serotonin system thereby establishing a serotonin biomarker for early stage drug screening. The objectives of this proposal are to a) enable precise measurements of serotonin's basal and evoked levels in vivo in the mouse hippocampus, b) establish the mechanisms that control extracellular serotonin in healthy and depressed mice and c) correlate chemical patterns in the way that different antidepressants modulate serotonin to clinical efficacy. Our preliminary data strongly support our proposed objectives. We have made significant progress in making the first measurements of basal and evoked serotonin in the hippocampus with fast scan cyclic voltammetry and a novel method, fast scan controlled adsorption voltammetry. Furthermore, we measured basal and evoked serotonin in depressed mouse models and found that serotonin levels are higher in depressed mice, proposing a mechanism for this finding via computational modeling. Finally, we mathematically correlated serotonin's extracellular response after different antidepressants to clinical efficacy and made an accurate prediction of efficacy for a known compound. This preliminary data allows us to make the central hypothesis that antidepressants influence extracellular levels of serotonin via different mechanisms, giving rise to unique serotonin signatures, which can be used to predict their likely degree of clinical efficacy. The rationale i that by correlating chemical serotonin patterns to clinical efficacy, a `serotonin biomarker' can identify the most clinically efficacious compounds. We take an interdisciplinary approach to test the central hypothesis via three specific aims: 1. Perform Fundamental FSCV Measurements of Serotonin's Ambient and Evoked Chemistry in the Hippocampus. 2. Establish Mechanisms Regulating In Vivo Extracellular Serotonin Levels in Healthy and Depressed Animal Models. 3. Correlate the Ability of Antidepressants to Influence Extracellular Serotonin (Ambient and Evoked) to Clinical Efficacy. The approach is innovative because it brings together advanced analytical techniques, mathematics and animal behavior to tackle important analytical challenges. The proposed research is significant because it can ultimately provide a means to screen antidepressants via a serotonin biomarker. Having an early stage screening tool is a powerful impetus for drug discoverers to resume their efforts in improving antidepressant therapies, benefiting individuals, society and the economy. The research will also have impact on a much broader research agenda. Serotonin measurement tools and working models of in vivo serotonin mechanisms can open and advance entire new lines of serotonin-related research.

Public Health Relevance

This proposal is relevant to public health because development of a biomarker for screening antidepressants is expected to transform the quality of effective antidepressant therapies, benefiting millions of depression sufferers in the USA. The proposed research not only impacts antidepressant therapies but makes available the tools to understand brain serotonin thus this proposal is relevant to the NIMH's mission relating to understanding the mind, brain, and behavior, and thereby reducing the burden of mental illness through research.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH106563-02
Application #
9297387
Study Section
Enabling Bioanalytical and Imaging Technologies Study Section (EBIT)
Program Officer
Nadler, Laurie S
Project Start
2016-06-15
Project End
2021-03-31
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
2
Fiscal Year
2017
Total Cost
$289,795
Indirect Cost
$56,386
Name
University of South Carolina at Columbia
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
041387846
City
Columbia
State
SC
Country
United States
Zip Code
29208