# Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes
## 1 Introduction  
Gene transfer, and the resulting expression of the encoded protein, has been used for decades in the treatment of disease, as well as for improvements in quality of life for those with genetic disorders [1]. There are several well-known methods for the delivery of genetic material: viral, physical, and nanoparticle-based. In the domain of physical transfection technologies, gene electrotransfer (GET) has become popular as an effective and relatively safe way to deliver genetic material to target tissues [2]. GET involves the use of pulsed electric fields (PEFs) to facilitate the transfer of exogenous genetic material, typically naked plasmid DNA, through the cell membrane to be translated by the host cell into a theraputically useful protein [3, 4]. A typical treatment involves the injection of the plasmid DNA, followed by the direct application of the prescribed PEFs to the desired tissue. The actual shape, magnitude, repetition rate, and count of these PEFs varies dependent on the plasmid DNA used, tissue to be treated, and physical configuration of the electrode used to deliver them [5, 6]. Gene transfer efficiency and resulting protein expression are highly dependent on selecting proper pulsing parameters and resulting electric fields [6]. Applying GET to various tissues in the body has also shown that expression is highly dependent on intrinsic tissue properties and, thus, no universal approach exists to date [7]. These parameters, both electrical and biological, have been the focus of previous, empirical optimization efforts. In the case of electrical parameters, the number of pulses, pulse width, applied voltage, repetition rate, and electrode configuration determine the magnitude, direction, and distribution of electric fields in the treated tissue. By changing any of these parameters, the amount and distribution of electrical energy delivered to the tissue can be modulated. These effects have been shown by modeling the underlying processes through the finite element modeling (FEM) of tissues, pulses, and electrode configurations [8; 9; 10], and the impact of changing these parameters has been shown in empirical optimization studies [11; 12]. There is a delicate balance between efficiently and transiently permeabilizing cells within tissues and overpulsing [13]. Overpulsing leads to permanent damage to cell membranes resulting in cell death, which is undesirable for gene therapy applications and enters the field of irreversible electroporation (IRE) or electrical ablation. Underpulsing, on the other hand, results in reduced cell permeabilization and insufficient or zero gene transfer efficiency [6; 14; 15]. Electrical parameters are not the only variables impacting the efficacy of gene electrotransfer success. Biological parameters such as tissue characteristics and the size of the plasmid DNA used can impact treatment success. Specific tissues under treatment, such as skin, muscles, or tumors, all have varying electrical properties that can greatly impact the distribution of electric fields during treatment [16]. Similarly, tissue impedance was found to change as a GET treatment progressed [17; 18; 19; 20], and these changes were not identical across all tissue types [21]. Thus, electric field distributions change as GET treatment progresses, depending on the underlying tissue type. Regarding the size of the plasmid DNA used, plasmid DNA with larger backbones was found to decrease transfection efficiency when compared to plasmid DNA with smaller backbones [5]. So, despite ultimately expressing the same protein, the smaller plasmid DNA allowed for higher transfection efficiency despite the same electrical parameters being used. Since the first treatments in the 1980s, the optimization of GET protocols has been an experimentally intensive endeavor. These experiments require large numbers of animals, usually mice, rats, and guinea pigs, but also validation in large animals such as pigs prior to progression to clinical trials.