Introduction: The neuromodulation field has seen many early successes in converting therapies into clinical application, but challenges remain in reducing off-target effects for new indications as well as targeting therapies to ensure clinical and commercial viability. Challenges have arisen validating preclinical successes in clinical settings due to the differences between preclinical and clinical subjects leading to different stimulation parameters required. These challenges have impeded the expansion of neuromodulation to new indications [Herring 2019]. Herein we aim to show how the information in the neural signals can be leveraged to quantify and avoid off-target effects [Ardell 2017] providing better specificity for future therapies. Outline of the project: We propose utilising existing SPARC data to build an efficient method of searching the stimulation parameter space using Bayesian Optimisation with Gaussian Processes as a data-efficient way of modelling the stimulation response function. Furthermore, we will build a machine learning model for mapping neural signals into a compressed representation (termed Neural Biomarkers) in order to interpret the body’s neural signals as effective measures of organ function. Finally, we validate both approaches in acute and chronic large animal models where stimulation parameters are searched extensively through the input space and further optimised against neural biomarkers derived from the neural signals.