University of Cambridge
Bioelectronics Lab

Diabetes is a disease caused by a breakdown in glucose metabolism, resulting in abnormal blood glucose fluctuations. Traditional therapies involve external insulin injection in response to elevated blood glucose to substitute the role of the pancreatic beta cells in healthy individuals. Limitations such as delays in external insulin action and the risk of hypoglycaemia, still have to be resolved to achieve optimal glycaemic control. Recently, bioelectronic medicine has emerged as a powerful strategy for treating diseases by electrical interfacing with peripheral nerves and organs. Incorporating bioelectronic technology in diabetes will enable improved computation of the insulin doses in anticipation and response to meals, therefore providing increased autonomy, and a reduction in diabetes-associated complications.

During my PhD studies, I developed and validated a computational framework for studying the neuro- metabolic interactions in rodent models. Results from this computational model inspired me to focus my postdoctoral studies on the development of a revolutionary pharmacological neuromodulation system for automated closed-loop control of patients with diabetes. This system will combine blood glucose measurements and neural signals decoded from peripheral nerves to determine the optimal insulin dose and the characteristics of the electrical stimulation to modulate glucose metabolism as desired. To successfully develop this technology, I aim to strengthen my knowledge in neurotechnology hardware and signal processing, which complements my background in modelling established during my PhD. Within the timeframe of the Fellowship, I expect to accomplish three main objectives:
1) To develop a new generation of safe and minimally invasive implantable devices that enable high-resolution stimulation/recording of peripheral nerves and organs, such as pancreas and liver, to modulate their metabolic function.
2) To quantitatively assess the impact of the electrical stimulation parameters on improving metabolic control.
3) To define an efficient electrophysiology analysis protocol that allows interpreting the neural signals
corresponding to the activity of target organs during meal intake.

The work planned for the duration of this Fellowship will enable the integration of the developed neural hardware and algorithms into a closed-loop platform that improves glycemic control in diabetes. It is my research vision and ambition to address one of the greatest challenges of medicine.