AI supports pharmaceutical production 4.0

1 April 2022

Pharmaceutical production is a complex and highly regulated process, fraught with the risk of error at every step. Whether it’s human error, cross-contamination or incorrect data processing, these threats can lead to production slowdowns or stoppages and, consequently, to additional costs. However, this trend is being reversed with the use of artificial intelligence and suitable algorithms.

Switching from conventional manufacturing to process automation generates an immense amount of data: production parameters, pressure measurements, temperatures, particle control measurements. Such vast amounts of information cannot be processed without the use of AI analysis. Algorithms look for identifiable patterns to detect industrial equipment failures or predict errors and then allow for real-time manufacturing parameter adjustments or scheduled maintenance. AI has a positive impact on the pharmaceutical industry. Production becomes more reliable and efficient, and the company more competitive.

Two concrete examples, Aizon and Stevanato Control, demonstrate the relevance of AI applied to pharmaceutical production, as Pep Gubau, CTO and co-founder of AI software provider Aizon, points out:  “Pharmaceutical manufacturers face a daunting challenge as they look to upgrade and automate the manual and paper processes that have run their businesses for decades. Manufacturers just starting their digital transformation journey first need to get access to siloed data, digitize the data and combine it to create key insights making it actionable. Our goal at Aizon is to give them a tangible starting point to use a greater variety of data to streamline operations through automation and process optimization ultimately improving yield and enhancing product quality.

Data integrity under the GxP umbrella

Pharmaceutical companies are obliged to secure their products and respect quality standards throughout the production process. To do this, they have many IT systems: production, quality management system, storage, or even shipping. They therefore require a common thread to guarantee the integrity and quality of the data, but also its use. This is where GxP, or “Good Practices”, come into play. These are not identical from one field of activity to another, but they have the same objective: to guarantee quality and security. In GxP, the abbreviation “x” is a variable referring to the fields of application of good practices. They include GLP (laboratories), GMP (production), GCP (clinical trials) and GDP (distribution). Although each country has its own regulations, GxP are harmonized because their requirements have been dictated by Europe and the FDA.