Traditionally, pharmaceutical manufacturing has been accomplished using batch processing with quality control testing conducted on samples of the finished product. This conventional approach has been successful in providing quality pharmaceuticals to the public for a number of years. Significant opportunities, however, exist for improving pharmaceutical development, manufacturing and quality assurance (QA) through innovation in product and process development, process analysis and process control.¹ Other sectors, for example, manufacturing and chemical, have already adopted new ways of thinking. The pharmaceutical industry, through a number of regulatory initiatives, is now beginning to develop these concepts.

Key points

There are many tools recommended within the PAT framework that enable process understanding for scientific, risk-managed pharmaceutical development, manufacture and QA.¹ These include multivariant tools for experimental design, data acquisition and analysis, process analysers (in-/on-/at-line) for the real-time monitoring of product quality, process control systems and knowledge management software applications. Much of the innovative thinking with respect to PAT and QbD is, however, focused on the development of novel probes/sensors for real-time and in-line monitoring of manufacturing processes.

Design of experiments

Traditionally, tools for multivariant data analysis, such as principal components analysis (PCA) and partial least squares projections to latent structures (PLS), have been used in conjunction with design of experiments (DoE) to investigate the underlying relationships in pharmaceutical processes.³ New knowledge engineering technologies based on artificial intelligence (AI) have, however, in recent times been developed that offer some advantages compared with conventional multivariant tools and can be used to compliment existing technology.^4,5

This article aims to highlight the value of using AI technology to understand complex multivariant systems such as pharmaceutical formulations and processes. Used appropriately, these tools enable the identification and evaluation of product and process variables that may be critical to product quality and performance, and allow the generation of predictive models that link changes in important/critical variables to changes in key product quality attributes — and ultimately clinical quality and performance.

The reality

"Perhaps we don't hear much about AI methods used within today's technologies because it's slightly unnerving when computers emulate human thinking. Yet we, and computers themselves, continue to improve the way AI works quietly in the background to optimize, reduce process costs, and improve timing and product quality. For some tough, nonlinear applications, AI may be the only solution."⁶

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. Many UK banks have used AI technologies to detect fraudulent behaviour by analysing transactions and alerting staff to suspicious activity. This has reduced fraud cases for the first time in nearly a decade by more than 5% to £402.4 million in 2004.⁷ In bioinformatics, AI has been used to manage, discover and interpret information/knowledge from biological sequences and structures. In the manufacturing industry, AI is used to achieve automotive control of production processes.