# A computational model to characterize the time-course of response to rapid antidepressant therapies

## TL;DR

This paper introduces a computational model to analyze how quickly and how long antidepressant treatments work, which could improve understanding of their effects.

## Contribution

The paper presents a new computational model to separately estimate the magnitude, speed, and durability of antidepressant response.

## Key findings

- The model parameters can be accurately estimated using nonlinear least squares even with up to 25% noise.
- Linear mixed effects models fail to disentangle effect magnitude and time course, unlike the proposed model.
- The model can accurately identify response components like magnitude, speed, and decay rate.

## Abstract

Our objective is to propose a method capable of disentangling the magnitude, the speed, and the duration or decay rate of the time course of response to rapid antidepressant therapies. To this end, we introduce a computational model of the time course of response to a single treatment with a rapid antidepressant. Numerical simulation is used to evaluate whether model parameters can be accurately estimated from observed data. Finally, we compare our computational modelling-based approach with linear mixed effects modelling in terms of their ability to detect changes in the magnitude and time-course of response to rapid antidepressant therapies in simulated randomized trials. Simulation experiments show that the parameters of our computational model can be accurately recovered using nonlinear least squares. Parameter estimation accuracy is stable over noise levels reaching as high as 25% of the true antidepressant effect magnitude. Comparison of our approach to mixed effects modelling using simulated randomized controlled trial data demonstrates an inability of linear mixed models to disentangle effect magnitude and time course, while our computational model accurately separates these response components. Our modelling approach may accurately identify the (A) magnitude, (B) speed, and (C) durability or decay rate of response to rapid antidepressant therapies. Future studies should fit this model to data from real clinical trials, and use resulting parameter estimates to uncover predictors and causes of different elements of the temporal course of antidepressant response.