Optimal Selection of Raw Materials for Pharmaceutical Drug Product Design and Manufacture using Mixed Integer Nonlinear Programming and Multivariate Latent Variable Regression Models

• Published in: Industrial & engineering chemistry research Vol. 52; no. 17; pp. 5934 - 5942
• Main Authors: Garcı́a-Muñoz, Salvador, Mercado, Jose
• Format: Journal Article
• Language: English
• Published: American Chemical Society 01.05.2013
• Subjects: Drugs; engineering; ingredients; Inventories; manufacturing; Materials selection; Mathematical models; Optimization; Raw materials; Regression; regression analysis; Stockpiling
• ISSN: 0888-5885; 1520-5045; 1520-5045
• DOI: 10.1021/ie3031828

This work presents a mathematical approach to make the most efficient use of historical data from development experiments (or commercial manufacture) to select the ingredients for the composition of a new product or to select the materials from inventory for the manufacture of a new lot of finished product. The method relies in the construction of a latent variable regression model that will serve to predict the result of combining certain materials. This predictive model is then embedded into an optimization framework to find the best combination among the pool of available materials according to a well-defined objective and subject to the predefined constraints. The framework is illustrated with two successful applications: the selection of the formulation and process for the design of a new solid oral drug product with high drug concentration and a continuous improvement project in commercial manufacture seeking to select the optimal set of lots of raw materials from the inventory to be mixed together in the manufacture a new lot for a controlled release product, given certain targets of dissolution levels and subject to material availability.