OpenEP

OpenEP predicts electric field distributions and electroporation outcomes for tumor treatments to support optimization of electroporation-based therapies such as electrochemotherapy (ECT), irreversible electroporation, and gene electrotransfer (GET).

Key Features:

• Electric field calculation: Calculates electric field distribution to identify regions where transmembrane voltage exceeds electroporation thresholds.
• Threshold and tissue dynamics modeling: Models variations in threshold values and temporal changes in electroporated tissue as functions of pulse number, intensity, length, and frequency.
• Pulse parameter specification: Allows specification of pulse parameters including number, intensity, length, and frequency for simulation studies.
• Geometry and material specification: Supports defining tissue type, electrode geometry, and electrode material within simulations.
• Critical pulse dosage computation: Computes critical pulse dosage required to achieve target electroporation coverage of the tissue.
• Shared memory parallelization: Implements a shared memory parallel computing approach to accelerate simulations and enable rapid parameter fine-tuning.

Scientific Applications:

• Electrochemotherapy (ECT): Predicts electric field coverage and electroporation probability to inform ECT protocol design.
• Irreversible electroporation: Models conditions for irreversible electroporation to support tumor ablation planning.
• Gene electrotransfer (GET): Simulates electroporation conditions relevant to GET to optimize membrane permeabilization for transfection.
• Oncological treatment planning: Supports optimization of electrode placement and pulse parameters for tumor treatment planning and efficacy assessment.
• Biotechnology and environmental research: Enables simulation studies in biotechnology and environmental science applications involving electroporation.

Methodology:

Computes electric field distributions and applies mathematical models of threshold variation and temporal electroporated-tissue changes, implemented with a shared memory parallel computing approach.