Mathematical Optimization of Tablet Formulations

Description

During development of new drug products, both the formulation and production process need to be optimized to achieve robust performance and deliver a final drug product that meets a series of critical quality attributes (CQAs). Formulation development is in many cases based on the formulator’s experience by means of trial-and-error which can potentially lead to increased experimental workload which induces additional costs and delayed time-to-market. This project aims to implement machine learning techniques into the drug development process to guide the experimentation during drug development which can lead to a reduction of the number of experiments that is needed to obtain a successful final drug product.

Researchers

Manuel Borja Lopez Pelaez

Manuel Borja Lopez Pelaez

Ph.D. Student
Jan Verwaeren

Jan Verwaeren

Assistant Professor

Publications