Build and deploy digital twins of pharmaceutical processes
Learn how Basetwo enables drug substance and drug product manufacturers to maximize yield, minimize product variances, and extend unit operation lifespansSee our platform
API manufacturing involves highly dynamic unit operations working collectively to produce a consistent, high-quality product. Careful coordination of these unit operations is needed to achieve efficiency at scale but can often be complicated by lack of online data at various stages:
Fill & Finishing
Basetwo enables manufacturers to understand and optimize the mechanisms driving drug substance manufacturing performance using an intuitive low code platform to link current and historical manufacturing data.
Using Basetwo, process engineers can build high fidelity predictive models to maximize reaction efficiency and understand the influence of process disturbances (i.e. agitation rate, temperature) to predict manufacturing efficiency and final product yield at multiple lab and commercial scales without any data science expertise.
Drug product manufacturing includes a variety of granulating, drying, milling, and compacting unit operations designed to create a consistent, cGMP-compliant finished product.
Final product quality is not only influenced by individual unit operation performance, but by upstream disturbances as well. Basetwo’s process modelling library allows manufacturers to easily create digital twins to integrate data across unit operations from initial mixing to final film coating, allowing for end-to-end manufacturing optimization.
Fill & Finish
Aseptic fill and finish is a critical part of manufacturing that involves largely repetitive, mechanical challenges to move, manipulate, and package liquid and solid dosage forms.
Communication along the supply chain is key, with contract manufacturers playing a major role in providing a safe and shipment-read product. Using Basetwo data ingestion pipeline and visualization integrations, engineers can monitor the status of their fill-finish machinery in real time and push manufacturing alerts to their team to prevent any costly delays to manufacturing.