Artificial Intelligence and Machine Learning Applied Interpretation and Analysis of Optimization Study and In-Vitro Release of Formulated Curcumin Loaded Nanoparticles

Abstract

Artificial Intelligence and Machine Learning studies of data collected during the research and review studies has been done using Jupyter Notebook (Anaconda) and MATLAB R2016a. Pie chart along with exclusive graphs like tree map, donut graph and others are used to make a comprehensive study of the data. Similar to Box Behenken the prediction model developed are exclusive models to predict the particle size and percentage entrapment efficiency of nanoparticles in any desired combination of working parameters as chosen. MATLAB R2016a which is a programming and numeric computing platform has been used for the graphical representations of Box behenken studies. The technique is very fast and accurate clearly depicting the analysis between the dependant and independent variables. Also MATLAB helps in simulation studies that has been incorporated in the introduction depicting the effect of curcumin that blocks Pet and EspC cytotoxicity on epithelial cells by preventing their release from the outer membrane. Curcumin-piperine loaded nanoparticles are prepared successfully using Fessi method. In vitro release data showed that Cu-Pi NPs showed Non-Fickian diffusion and sustained release mechanism for the release.