Artificial Intelligence in Pharmaceutics

Background

We address the problem of determining from laboratory experiments the data necessary for proper modeling of drug delivery and efficacy in anticancer therapy. There is an inherent difficulty in extracting the necessary parameters because the experiments often yield an insufficient quantity of information. To overcome this difficulty, we propose to combine real experiments, numerical simulation, and Machine Learning (ML) based on Artificial Neural Networks (ANN), aiming at a reliable identification of the physical model factors, e.g., the killing action of the drug. Mathematical modeling approaches powered by Machine learning algorithms provide estimations of the quantitative behavior of anticancer agents by characterizing the pharmacokinetics and pharmacodynamics of the drug to achieve optimized targeted drug delivery. By considering the number of healthy cells in our body, an optimized proposed model will predict the optimum dosage of the drug. In order to resolve various drug barriers, resistance and improve tissue selectivity, this computational method will analyze the biophysical and biochemical properties of the tumor cells to address various aspects of drug penetration. So, by taking into account all the biological variables, a model mathematical structure can help to create a potent targeted delivery mechanism.