Machine Learning - The Future Of Pharma

Faster process optimisation using ML enabled workflows

Real processes have numerous objectives.  Being able to locate the optimal trade-off point where all targets are met is no easy task. Machine Learning enabled workflows make this easier than ever before.

Use previous experimental results to create models that can select the next most important experiment:

• Utilise online analytics / PAT
  • Faster optimisation and no time waiting for lab results, by coupling the ML process with real-time analytics
• Exploit machine learning and self-optimisation to automatically guide a process towards the optimum
  
• Processes can be left unattended to find their own optimum settings, freeing up scientists and engineers for more important tasks
• Use true multi-objective optimisation to establish the trade off between CQAs and environmental / economic targets

Use this data to build control models, moving from optimised to controlled in a single step. 
Integrate with mechanistic or hybrid process models:

• Optimise process parameters in a digital twin before wasting materials on a live process
• Carry out in-silico experimental campaigns to hone process parameters before making the move to a live process. Increasing confidence that the process will succeed
• Discover critical variables and eliminate unimportant ones prior to an experimental campaign 
• Exploratory experimentation on a live process is expensive and time consuming, eliminate this burden using hybrid process modelling