Criterion AI is a versatile toolbox that can be used to solve many mission-critical tasks in the production of pharmaceutical drugs. Here, we present five solutions built with Criterion AI in different areas of the drug manufacturing process.
The production of active pharmaceutical ingredients is a delicate process and even the smallest deviations can lead to the scrapping of entire batches. AI models (such as artificial neural networks) can help monitor all of the variables in API production and control them in real time in order to produce the golden batch each and every time. 🏆
Criterion AI’s model zoo hosts AI models specifically designed to support the production APIs in many types of forms (e.g., amorphous and crystalline) that can be trained right away.
Pharmaceutical formulators rely on data from titration machines, centrifuge machinery, mixers and tanks that have in-built sensors in order to conduct the formulation process.
However, these data are often hard to interpret and the rules-based statistical tools employed today to process the data are often too simplistic and not powerful enough to provide information that is accurate and actionable enough for formulators to reliably act upon.
AI models are much better at modeling the formulation process and thereby deliver much more intelligible insights.
Filling and tableting
Carefully controlling the filling or tableting process is key to ensuring high quality levels of finished products. Managing all of the individual variables that goes into filling/tableting is often conducted more as an art rather than a science, leading to deviations across batches.
AI models are capable of extracting signals from many more variables than traditional statistical algorithms as well as understanding the intercorrelations among them in order to provide much more valuable feedback to operators, enabling them to produce better batches more consistently.
The quality control processes of drug manufacturing are often driven by a lot of manual labor and, as a result, incur a lot of costs and risk of human error.
Almost all types of quality control (including environmental control, visual inspection, calibration of monitoring and measuring devices, etc.) have the potential to be automated by the use of AI, which can lead to both higher detection rates of product rates and lower false rejection rates. The ultimate result is higher patient safety and reduced operational costs.
Packaging is the final step of the manufacturing process and, thus, scrapping products at this stage is often very expensive. Ensuring that cartridges or syringes are properly assembled into their devices and subsequently put into their packages is key to keeping up healthy gross margins.
AI models significantly outperform traditional computer vision solutions in terms of accuracy, reliability, speed, and maintainability. Whether dealing with label inspection or assurance of correct assembly, high-speed cameras can be combined with convolutional neural networks to keep both false rejection rates and need for maintenance close to zero.