An iterative update
We have recently developed a one-pager that describes our (draft) recommendations in a comprehensive and clear way. Now, we are deep into developing potential partnerships on these recommendations, so please feel free to contact us and let us know what you think! Other than soliciting information electronically, we have been going to stakeholders and presenting our recommendations to get feedback and develop potential partnerships. The feedback so far has been positive in terms of our recommendations being critical and different flavors of feedback are shown below.
Recently, we presented our machine learning results and we also led a design session on Reduce, Reinvest, Recalibrate with the Forum on Drug Discovery, Development, and Translation at the National Academy of Medicine, which was our second session with the group. We used design to quickly engage the participants on how our results are relevant, if there are other factors we should be thinking about, and how to best cause change in the system by our recommendations. This resulted in over 20 different implementation ideas as well as over 50 sticky notes with feedback. Overall the forum agreed that we need indicators of trials that are more likely to fail before the trial starts and we need investments that incentivize not doing these trials, as opposed to the current system where due to the risk present in drug development, companies are forced to take, as one of the participants said, “more shots on goal.”