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Lonza Launches Design2Optimize for Faster Small Molecule API Development

New model-based platform is designed to accelerate process development of active pharmaceutical ingredients.

Lonza, a contract development and manufacturing organization (CDMO), has launched its new Design2Optimize platform to enhance process development and manufacturing of small molecule APIs.

The platform is based on an optimized design of experiments (DoE), which is a statistical method for enhancing process and reaction conditions. Aiming to drastically shorten the development timeline for small molecule APIs, the Design2Optimize platform employs a proprietary model-based approach that guides experimental setup based on optimal conditions.

The model-based platform combines physicochemical and statistical models with an optimization loop to enhance chemical processes with fewer experiments than traditional statistical methods. Developed with the Fraunhofer Institute for Industrial Mathematics (ITWM), the Design2Optimize platform helps drug developers to build predictive models at speed, even for complex or poorly understood reactions where the mechanism is not definitively known. By generating a digital twin of each process, the platform enables scenario testing in situ without the need for further physical experimentation. By significantly reducing experimentation time and resource use, Design2Optimize accelerates the path to manufacturing and increases the likelihood of right-first-time processes.

Combining the Design2Optimize platform with Lonza’s AI-enabled route scouting service and high-throughput experimentation (HTE) to rapidly test and compare reaction conditions, Lonza’s integrated approach streamlines development, enhances process understanding, and accelerates the journey from molecule design to market.

Jan Vertommen, Vice President of Commercial Development, Advanced Synthesis, Lonza, said, “Lonza is transforming the development of small molecule APIs by advancing innovative offerings targeting faster development and optimization of synthetic API manufacturing processes. With the rising complexity of APIs in mind, these new offerings aim to reduce the time it takes to move from early candidate selection to first-in-human materials.”

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