Real time electroporation control for accurate and safe in vivo non-viral gene therapy.

In vivo cell electroporation is the basis of DNA electrotransfer, an efficient method for non-viral gene therapy using naked DNA. The electric pulses have two roles, to permeabilize the target cell plasma membrane and to transport the DNA towards or across the permeabilized membrane by electrophoresis. For efficient electrotransfer, reversible undamaging target cell permeabilization is mandatory. We report the possibility to monitor in vivo cell electroporation during pulse delivery, and to adjust the electric field strength on real time, within a few microseconds after the beginning of the pulse, to ensure efficacy and safety of the procedure. A control algorithm was elaborated, implemented in a prototype device and tested in luciferase gene electrotransfer to mice muscles. Controlled pulses resulted in protection of the tissue and high levels of luciferase in gene transfer experiments where uncorrected excessive applied voltages lead to intense muscle damage and consecutive loss of luciferase gene expression.

The course of tissue permeabilization studied on a mathematical model of a subcutaneous tumor in small animals.

One of the ways to potentiate antitumor effectiveness of chemotherapeutic drugs is by local application of short intense electric pulses. This causes an increase of the cell membrane permeability and is called electropermeabilization. In order to study the course of tissue permeabilization of a subcutaneous tumor in small animals, a mathematical model was built with the commercial program EMAS, which uses the finite element method. The model is based on the tissue specific conductivity values found in literature, experimentally determined electric field threshold values of reversible and irreversible tissue permeabilization, and conductivity changes in the tissues. The results obtained with the model were then compared to experimental results from the treatment of subcutaneous tumors in mice and a good agreement was obtained. Our results and the reversible and irreversible thresholds used coincide well with the effectiveness of the electrochemotherapy in real tumors where experiments show antitumor effectiveness for amplitudes higher than 900 V/cm ratio and pronounced antitumor effects at 1300 V/cm ratio.

Sequential finite element model of tissue electropermeabilization.

Permeabilization, when observed on a tissue level, is a dynamic process resulting from changes in membrane permeability when exposing biological cells to external electric field (E). In this paper we present a sequential finite element model of E distribution in tissue which considers local changes in tissue conductivity due to permeabilization. These changes affect the pattern of the field distribution during the high voltage pulse application. The presented model consists of a sequence of static models (steps), which describe E distribution at discrete time intervals during tissue permeabilization and in this way present the dynamics of electropermeabilization. The tissue conductivity for each static model in a sequence is determined based on E distribution from the previous step by considering a sigmoid dependency between specific conductivity and E intensity. Such a dependency was determined by parameter estimation on a set of current measurements, obtained by in vivo experiments. Another set of measurements was used for model validation. All experiments were performed on rabbit liver tissue with inserted needle electrodes. Model validation was carried out in four different ways: 1) by comparing reversibly permeabilized tissue computed by the model and the reversibly permeabilized area of tissue as obtained in the experiments; 2) by comparing the area of irreversibly permeabilized tissue computed by the model and the area where tissue necrosis was observed in experiments; 3) through the comparison of total current at the end of pulse and computed current in the last step of sequential electropermeabilization model; 4) by comparing total current during the first pulse and current computed in consecutive steps of a modeling sequence. The presented permeabilization model presents the first approach of describing the course of permeabilization on tissue level. Despite some approximations (ohmic tissue behavior) the model can predict the permeabilized volume of tissue, when exposed to electrical treatment. Therefore, the most important contribution and novelty of the model is its potentiality to be used as a tool for determining parameters for effective tissue permeabilization.

Comprehensible evaluation of prognostic factors and prediction of wound healing.

We analyzed the data of a controlled clinical study of the chronic wound healing acceleration as a result of electrical stimulation. The study involved a conventional conservative treatment, sham treatment, biphasic pulsed current, and direct current electrical stimulation. Data was collected over 10 years and suffices for an analysis with machine learning methods. So far, only a limited number of studies have investigated the wound and patient attributes which affect the chronic wound healing. There is none to our knowledge to include treatment attributes. The aims of our study are to determine effects of the wound, patient and treatment attributes on the wound healing process and to propose a system for prediction of the wound healing rate. First we analyzed which wound and patient attributes play a predominant role in the wound healing process and investigated a possibility to predict the wound healing rate at the beginning of the treatment based on the initial wound, patient and treatment attributes. Later we tried to enhance the wound healing rate prediction accuracy by predicting it after a few weeks of the wound healing follow-up. Using the attribute estimation algorithms ReliefF and RReliefF we obtained a ranking of the prognostic factors which was comprehensible to experts. We used regression and classification trees to build models for prediction of the wound healing rate. The obtained results are encouraging and may form a basis for an expert system for the chronic wound healing rate prediction. If the wound healing rate is known, then the provided information can help to formulate the appropriate treatment decisions and orient resources towards individuals with poor prognosis.