4 ms·
In short, this paper calls for "Do more math on the front-end of the pipeline." The core issue is that CEOs of biopharmas aren't compensated as such, so if you
by kumarski 11y ago
In short, this paper calls for "Do more math on the front-end of the pipeline."
The core issue is that CEOs of biopharmas aren't compensated as such, so if you were to go to one with mathematical techniques that would take 3 to 6 months to run, (Combining research, bioinformatics, and diff EQ) they would look at you like you're crazy because the timeline and their incentivization is skewed toward not failing a trial.
Trials exceed CEO lifetime, and that's one core issue as to why pharma development isn't done this way today.
- adenadel 11y agoI think when they were referring to models they weren't necessarily referring to mathematical models, but also experimental models. They more stressed the importance of the predictive models that you are using, which in biomedical research is typically an experimental rather than mathematical/computational model. From the takeaways portion of the article "Judging the quality of the 'compass' (i.e., the 'validity' of experimental models) is very difficult. However, the drug industry should put its brightest and best people onto the problem of model validity"
- zevets 11y agoI didn't get that at all from either the interview or the paper. From the paper, the conclusions seem to be: Decision theory creates a logically grounded explanation as to why high predictive value experiments are being done less often. To resolve this, research needs to be done in measuring the effectiveness of drug discovery models. Nowhere in the article do they call for more math - the final conclusion of the paper is a call for more experiments validating existing drug models.