In drug manufacturing, a golden batch is used to describe the ideal batch of a drug. Getting a proper yield is very critical and it is key to capture the parameters of the ideal circumstances under which a golden batch is produced. These parameters include temperature, air conditioning, overall steam, machine effectiveness, equipment life, etc. The interrelationship between these parameters is critical to understand in order to be capable of producing repeat batches with high consistency.

Traditional statistical models can usually only capture linear or quasilinear relationships of few variables (typically, a number in the order of hundreds or in the low thousands), which isn’t going to cut it. For that reason, pharmaceutical manufacturers continue to struggle with controlling the recipe for the golden batch.

An artificial neural network is capable of analyzing millions of raw and derived parameters in a matter of milliseconds, which means that continuous analysis of the parameters collected from the API production equipment and its environment can be conducted in real time and adjustments can be made accordingly.

In Criterion AI, you can train a wide range of preimplemented AI models designed specifically for cases in the API production stage. These models capture all of the inputs typically produced by API production equipment and make use of these to accurately represent the current state of the batch and to provide real-time recommendations for how to control the batch. This empowers the operators to produce golden batches again and again with high consistency, no matter their level of experience.

With Criterion AI you can

  • Train AI models to control the API production process
  • Match the golden batch profile more consistently
  • Increase efficiency and yield in the API production stage
  • Streamline production operations across multiple sites
  • Simplify manual processes around API production

Get in touch with us to learn more about how to get started with Criterion AI to improve API production.

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