The design, development and implementation of a robust, predictable, and scalable manufacturing process is the key outcome of chemical development. In chemical development, laboratory automation plays an important role—enabling the collection of large experimental arrays directed towards the discovery and optimization of process conditions that yield robust, predictable, and scalable manufacturing processes. To take a relevant example, rationally-designed and automatically-executed experiments can be used to accelerate the discovery of new reactions and build unprecedented understanding around chemical reaction dynamics. Augmenting these experimental tools are new computational techniques in data science and machine learning that enable scientists and engineers to extract insight and models that inform process development. In this webinar, our speakers from Amgen and Bristol-Myers Squibb will discuss approaches to efficiently generate, and effectively utilize, chemical reaction data through automation and data-driven modeling.
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In the first talk, Victor Rosso and Jacob Albrecht from Bristol-Myers Squibb will discuss how laboratory automation plays an important role in enabling the rapid identification and optimization of reaction conditions. Through specific examples, this presentation will highlight key aspects of the application of modern, automated technologies towards design of experiments (DoE) and the statistical modelling of their processes as they enter manufacturing environments. In the second talk, Dan Griffin from Amgen will describe how automation can be combined with new strategies for learning from data. Using a real-world example, this presentation will demonstrate how such an approach can accelerate chemical reaction modeling and optimization for mid-phase pharmaceutical process development. Combined, these presentations clearly outline a data-driven development paradigm that has advantages in both speed and cost-of-development.
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