AI-models tend to be variable and complex and are often associated with lack of transparency in the algorithms that are deployed. This impacts the reproducibility of results and prediction, leading to lack of consistency, says Vandana Iyer, Research Director-TechVision, Frost & Sullivan.

There are a wide range of automation technologies that are being developed for the pharmaceutical industry. These technologies can largely be classified into:

Some of the key barriers to automation adoption include:

There is a rising need to define regulatory standards for the use of AI and automation across the global pharmaceutical landscape. Regulatory agencies such as the US FDA are already drafting guidelines such as ‘Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products’ to ensure structured regulatory standards which helps improve compliance. Continuous monitoring of quality standards, partnering with vendors that have the necessary regulatory compliance, such as the 21 CFR Part 11 for US FDA helps improve outcomes for automated processes. Ensuring and implementing GAMP5 (Good Automated Manufacturing Practice) guidelines and following ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, and Available) help improve regulatory compliance for automation. Conducting regular audits, securing virtual data repositories, rigorous documentation and periodic risk-based validation are also some practices that can be adopted to improve regulatory compliance.

Some of the key automation trends in pharma will be:

Scalability in automation offerings can be achieved by:

As AI technologies evolve at a rapid pace, it is necessary to continuously upskill personnel and upgrade equipment and software periodically to ensure seamless manufacturing at the desired scale.