• anatomically accurate
• computational models
• hybrid models
• in-silico
• near-organ
• neuromodulation

Computational models are valuable tools for accelerating the design and optimization of bioelectronic therapies for drug-resistant pathologies. While open-source modeling frameworks exist for studying peripheral implants placed around or within nerves, they lack the capability of representing non-neural anatomical structures. This hinders the in-silico investigation of novel bioelectronic modalities, such as endovascular and near-organ neuromodulation, which rely on indirect electrical coupling between implants and target nerve fibers.
To overcome this limitation, we propose a versatile software platform to instantiate anatomically accurate models of neuromodulation of the peripheral nervous system, accounting for the complex geometries and electrical properties of realistic tissues. The platform builds upon the hybrid modeling toolbox for peripheral nerve interfaces developed within the TNE Lab at Scuola Superiore Sant’Anna and EPFL.
We verify the software implementation by replicating state-of-the-art computational models of bioelectronic therapies, such as clinical vagus nerve stimulation and neuromodulation of the splenic neurovascular bundle. Additionally, we conduct a preliminary investigation into the feasibility of modulating the renal nerve activity with endovascular leads, which offers promising new treatments for hypertension.
This framework broadens the repertoire of neuromodulation modalities that can be explored using open-source modeling tools, thereby facilitating the development of next-generation bioelectronic therapies.