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Employing DoE judiciously can streamline the experimental workflow, yielding accurate results with fewer trials, which in turn conserves time, resources, and materials. However, its effectiveness is contingent upon proper application; misuse can result in misleading inferences and suboptimal decisions. This presentation aims to demystify the core principles of DoE, emphasizing the critical best practices and pitfalls to avoid, particularly in the realm of drug development.
We will delve into the strategic use of DoE, from preliminary screening designs to more intricate methodologies, illustrated through specific case studies in pharmaceutical research. By dissecting these examples, the presentation seeks to equip researchers with the knowledge to leverage DoE effectively, fostering more informed decision-making in drug development processes.
Learning Objectives:
Learn differences between DoE, Machine Learning, AI
Concepts involved in Design of Experiments
Appropriate use of Design of Experiments and learn from case studies
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About this item
The Design of Experiments (DoE) methodology is a robust framework for optimizing and elucidating complex processes across analytics and formulations. It transcends the conventional one-variable-at-a-time (OVAT) approach, offering a structured strategy to dissect the interplay between multiple factors influencing a process and its outcomes. This multifaceted examination not only unveils the effects of individual factors but also illuminates their interactions, enhancing the understanding of the process's behavior.
Employing DoE judiciously can streamline the experimental workflow, yielding accurate results with fewer trials, which in turn conserves time, resources, and materials. However, its effectiveness is contingent upon proper application; misuse can result in misleading inferences and suboptimal decisions. This presentation aims to demystify the core principles of DoE, emphasizing the critical best practices and pitfalls to avoid, particularly in the realm of drug development.
We will delve into the strategic use of DoE, from preliminary screening designs to more intricate methodologies, illustrated through specific case studies in pharmaceutical research. By dissecting these examples, the presentation seeks to equip researchers with the knowledge to leverage DoE effectively, fostering more informed decision-making in drug development processes.
Learning Objectives:
- Learn differences between DoE, Machine Learning, AI
- Concepts involved in Design of Experiments
- Appropriate use of Design of Experiments and learn from case studies
Speaker Information
Rajiv Nayar, Ph.D.
HTD Biosystems, Inc.