(354c) Machine Learning in Pharmaceutical Process Development: Where Are We up to in Pharma 4.0?

Artificial Intelligence (AI) and Machine Learning (ML) have become ubiquitous terms in the past couple of years. This presentation inspects and compares the current AI, ML approaches with alternative techniques which are already well established within the process industries, and now also in many of the innovative Pharmaceutical companies.

Advanced Process Control (APC) and Multi-Variate Analysis are data driven techniques to build models, understand the product and process, monitor for abnormal operational events and more recently directly adjust the process to achieve closed loop control of product Critical Quality Attributes (CQAs). When we compare these “traditional” data-driven techniques with Machine Learning we see the same algorithms being applied and the common goal of improved decision making through data analysis, prediction and adjustment. There is a difference however, and we’ll explore what it is within the talk.

Case studies from the Pharmaceutical industry are used to demonstrate the application of several forms of Machine Learning for process control and optimisation. By comparing these Machine Learning algorithms with other tools in the process optimisation “toolbox”, we can examine the benefits and challenges to implementing this technology within the industry.