Multi-parametric Statistical Analysis of Economic Data for Continuous Pharmaceutical Manufacturing

The global pharmaceutical industry faces high R&D, regulatory and cost pressure which can be alleviated by the advent of Continuous Pharmaceutical Manufacturing (CPM). Embarking upon demonstrating and commissioning continuous processes is not trivial: judicious product selection and process design is quintessential for viable investments. Technological as well as economic considerations must be inseparably combined, but a quantitative method for elucidating only the most viable candidates has yet to emerge. This study illustrates how systematic statistical analysis can support business decisions and process R&D for the synthesis and design of continuous pharmaceutical processes. A systematic statistical evaluation of UK economic data has been performed to identify viable drug substances (DS) and drug products (DP) for continuous manufacturing. Product classification and ranking is employed to select those with the highest demand, and statistical hypothesis testing explores causality and correlations of key parameters. Molecular weight and complexity have been correlated with trade and value statistics, indicating that amides, lactones, antibiotics and hormones have high CPM potential.