FDA advocates building quality into a product through PAT.

The biggest benefit of PAT and the current FDA initiatives may be in allowing pharmaceutical scientists to use their technical capabilities to improve pharmaceutical processes.

Six sigma, process analytical technology (PAT) and related initiatives are driving greater use of statistical analysis methods to increase process understanding and improve manufacturing capabilities.

To date, calorimetry has not been given a fair trial in the PAT arena. Recent advances in reactor technology and design will ensure that real-time calorimetry is the present and future of PAT.

Recent regulatory initiatives have emphasized the need to improve pharmaceutical manufacturing. PAT marked the beginning of a number of regulatory efforts to encourage innovation and a transition towards science-based manufacturing. This article reviews the progress of the regulatory initiatives and describes two significant research initiatives to develop a future pharmaceutical manufacturing environment based on scientific understanding of pharmaceutical materials and processes.

A new Raman spectroscopic method to detect magnesium stearate in powder blends and tablets is described. High-volume pharmaceutical manufacturing requires the use of lubricants to facilitate tablet ejection from compressing machines. However, lubricants may also bring a number of undesired problems that have been widely documented in pharmaceutical scientific literature. New analytical methods are needed to understand lubrication and provide process knowledge in support of FDA's process analytical technology initiative. The detection of magnesium stearate in lactose, mannitol, corn starch and other commercially important excipients is reported. The Raman spectroscopic method has a detection limit of about 0.1% (w/w) based on the 2848 cm-1 band that corresponds to the symmetric stretch of the methylene group in magnesium stearate.

In biomanufacturing, multiple sensors provide a wealth of data that could be used to enhance process understanding and assist in performance improvement. This article looks at how to move from a data-rich environment to one where the data are translated to useful information that leads to knowledge and, ultimately, process improvements.

In recent years, AI has become important in a number of fields in helping to make better use of information, increasing efficiency and enhancing productivity.

EFPIA's 'Mock P.2' document aims to show how the role of 'quality risk management' and process analytical technology as an enabler for quality by design can be presented in a common technical document format. This article summarizes the main features of this document, and explains the key concepts and principles used.