Data-driven tiered procedure for enhancing yield in drug product manufacturing
Journal article, 2016

Enhancing efficiency of pharmaceutical batch production processes is an important challenge in times of increasing public pressure on healthcare costs and decreasing research productivity. This study presents a data-based procedure for systematic yield enhancements in drug product manufacturing, based on four steps. On the first step, production is reviewed to select relevant loss causes, which are assessed on the second step deductively with the goal of assigning measurable parameters. Descriptive Statistical Modelling of loss causes is then performed on the third step, enabling model-based enhancements of processes on the fourth step or, if necessary, a loop-back review of a given loss cause. An industrial case study was performed on production data of 88 batches and demonstrated the applicability of the procedure by prioritizing relevant loss causes, reducing required sample quantities by up to 8% and a cosmetic defect by about 70% by a process change.

Multivariate data analysis



Industrial case study

Sterile drug product manufacturing


L. Eberle

Swiss Federal Institute of Technology in Zürich (ETH)

F. Hoffmann-La Roche AG

H. Sugiyama

University of Tokyo

Stavros Papadokonstantakis

Chalmers, Energy and Environment, Industrial Energy Systems and Technologies

A. Graser

F. Hoffmann-La Roche AG

R. Schmidt

F. Hoffmann-La Roche AG

K. Hungerbühler

Swiss Federal Institute of Technology in Zürich (ETH)

Computers and Chemical Engineering

0098-1354 (ISSN)

Vol. 87 82-94

Areas of Advance


Life Science Engineering (2010-2018)

Subject Categories

Chemical Engineering



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