PD2M: 

Pharmaceutical Discovery, Development and Manufacturing Forum 

Moving APC Forward in the Pharma Industry


Machine Learning in Pharmaceutical Process Development:
The State of the Art


30 September - 1 October, Bethesda, MD, USA

John Mack - Perceptive Engineering

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American Institute of Chemical Engineers (AIChE)

AIChE works across a wide range of industrial sectors, from petrochemicals to pharmaceuticals, from bioprocessing to nanotechnology.
This conference will focus on the challenges, opportunities and best practices of implementing advanced process control within the pharma sector.
Perceptive Engineering have been invited to present, because of their experience and leadership in this area.

ABSTRACT
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 and ML approaches with alternative techniques that 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 of the process that provide greater insight and understanding, then use those models to monitor for abnormal operational events and, more recently, directly adjust the process to achieve closed loop control of product 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 and other industries will be used to demonstrate the application of several forms of Machine Learning for process control and optimisation. The case studies present applications for the rapid development of synthesis and crystallisation processes.

Our Clients & Partners

Selection of our clients and key partners we work with to improve process efficiency