Novel Alternative Methods (NAMs) are emerging as an important strategy for improving the success rate in drug discovery. This requires that in vitro models much more accurately and reliably predict the effect of drugs when administered to a human.
We will review approaches that are being taken to achieve these goals, including the development of more physiologically relevant and translational cellular models, improvement of existing “gold standard” technologies, and future strategies that have the potential to incorporate much larger data sets than any animal model ever could.
Key Learning Objectives:
- What NAMs are and why they are needed
- Where biological models are expanding to meet this need
- How existing platforms can be optimized for use as NAMs
- How artificial intelligence (AI) has the potential to impact NAMs
- Where we can look to further expand NAM predictability