Designing and implementing consumer research and clinical studies is expensive, intrusive, and can introduce critical delays in getting products to market. The stringent regulatory requirements for clinical trials make them a particularly challenging example of this problem. Software tools are providing business teams with the ability to optimise and select the right study designs through deep learning insights, reducing cost and time of development, while improving confidence in development decisions. In this webinar, we’ll see how machine learning (ML) can enable faster progress and insightful learnings, drawing on experience including a project at BAT that applying ML as a tool when optimising clinical studies on the pharmacokinetic response of participants using a new product.
Click below to view the recorded webinar.
Original broadcast: Tuesday, 27 Sep, 2022
Speakers:
- Emma Green is an experienced ClinDev executive and advisor to biotech
- Dr Joel Strickland is a Machine Learning Scientist at Intellegens
Unfortunately, our guest speaker from BAT was unable to join us to present their case study. The case study is covered briefly in the recording but we aim to arrange a more detailed presentation of this material at a later date.
- Alchemite™ for clinical studies
- Alchemite™ for life sciences
- An overview of the Alchemite™ Technology
- Case studies
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