Accelerated Product and Process Development with AI

"Applying machine learning to materials development can not only improve R&D cost and efficiency but also help enable new business models and greater agility."

Chemical Processing Magazine

New product introduction is the key competitiveness factor for all process manufacturers, but process development (PD) capacity is not sufficient to keep up with the increasing demand. Lack of PD capacity becomes a bottleneck to much needed manufacturing capacity.

The complexity of molecules – chemical, biologic, and biochemical – is increasing the complexity of data relationships. With legacy software, statistical tools, and DoE methods at their disposal, PD teams are continually under pressure to meet the demand.

Machine Learning (ML) can accelerate PD, but large amount of data required by most ML algorithms is never available during PD. Fortunately, the inherent experimental nature of PD and prior knowledge is always available.

With our sample-efficient, small-data AI, Quartic has revolutionized the way PD is done and will be done in the future. Leaders in the industry are using this application to complete PD campaigns with unprecedented speed.

"The PD optimizer gives us the ability to find optimal batch conditions in fewer runs, reducing raw materials costs and shaving weeks off our timeline- ultimately driving better productivity across the PD campaigns"
- PD scientist at global pharmaceutical company


  • Higher productivity of PD teams and PD campaigns
  • Reduction in material consumption
  • Data and evidence-based CMC and control strategy design using final characterization outcomes
  • Digital Tech Transfer ready
  • Process investigations and improvement can be linked back to the PD outcome for design space reference


  • Small-data, sample efficient AI leverages scientists’ knowledge and hypothesis
  • Completely explainable
  • Iterative characterization hypothesis testing
  • Outcome (such as titer) prediction before applying the optimized recommendations
  • Completely digital workflow, digital records of experiments and campaigns
  • GxP compliance
  • Native PAT data consumption

Additional Content

Using AI to Optimize Process Development – A New Paradigm for Efficient PD

Chris Demers, Ph.D., Principal Scientist at Catalent, Suresh Ramanan, Associate Principal at ZS, and Vinodh Rodrigues, Life Sciences Product Owner at, will share the benefits of digitalization and optimization in PD for accelerating and driving efficiency in the early stages of the product journey.

Watch video →

Optimizing DoE and Production Runs with Little Data

In this blog chief data scientist Xiaozhou Wang, illustrates the improvement that Bayesian Optimization process provides for DoE and production runs in batch processes.

Read article →

Download PD Optimizer application note

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