The application of machine learning algorithms in understanding the effect of core/shell technique on improving powder compactability.

This study systemically investigated the application of core/shell technique to improve powder compactability. A 28-run Design-of-Experiment (DoE) was conducted to evaluate the effects of the type of core and shell materials and their concentrations on tensile strength and brittleness index. Six machine learning algorithms were used to model the relationships of product profile outputs and raw material attribute inputs: response surface methodology (RSM), Support Vector Machine (SVM), and four different types of artificial neural networks (ANN), namely, Backpropagation Neural Network (BPNN), Genetic Algorithm Based BPNN (GA-BPNN), Mind Evolutionary Algorithm Based BPNN (MEA-BPNN), and Extreme Learning Machine (ELM).

Maximizing the Impact of Physiologically Based Oral Absorption Modeling and Simulation.

The challenge of bringing innovative medicines to patients in combination with intense competition within the pharmaceutical industry has induced companies to develop quality medicines more efficiently and cost-effectively. State-of-the-art approaches to advance drug development have never been so urgent. One such approach that has been gaining traction within the industry is the application of modeling and simulation.