Computational Tools Use and Implementation Throughout a Product Life-Cycle in the API Industry
Nir Haimovich, Process Technology Global TAPI R&D, TEVA API, Petach-Tikva, Israel
Guy Samburski, Process Technology Global Tapi R&D, Teva API, Petach-Tikva, Israel
The common API (Active Pharmaceutical Ingredient) industry relies mainly on synthetic organic chemistry processes, involving a sequence of chemical engineering unit-operations, e.g., chemical reaction, distillation, crystallization, and more. While early stages of the development include screening and preliminary optimization, later stages are focused on process scale-up at the pilot scale, followed by a final technology transfer to the production site. At industrial scale, producing the final API is comprised of a series of these previously developed up/down-stream operations, of either batch or (more recently also) continuous processes. These processes are executed and closely monitored by online sensors and controls, off-line analytics, and filled-in batch-cards - all audited on a regular basis by both internal and regulatory entities (e.g., FDA). Overall process development and analysis commonly include various considerations: yield and chemical quality, safety, operational and raw material cost, etc., in light of market needs.
In recent years, the API industry has started to realize the underlying potential in the use of various computational approaches throughout its product life-cycle: from early development to optimizing/altering existing production line performance, capacities and cycle-times. In this talk we describe TEVA API's use and implementation of computational tools throughout the API product life-cycle: DOE (Design of Experiments) using the JMP software at the early stages of development towards screening and optimization, (mechanistic) calibrated model-based process simulations using DynoChem, Visimix and CHEMCAD allowing for comprehensive analysis and understanding crossing all stages (and supporting technology transfer), and ending with big / data analysis for multiple-batch production data using MATLAB/JMP. A few examples will be combined in the presentation, demonstrating enhancement of R&D, incorporating new technologies in an educated way, and facilitating optimized and more robust processes - allowing for better and safer design based on applying computational tools, while training people to use them
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