I agree the best parameters in a DCF should probably be based upon an analysis of historic data, but I don't agree that its hard stuff.
As I've already posted previously, this analysis has already been done for us - there is published research analysing a truly enormous datasets, such as https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6409418/ which analysed >185,000 drug phase transitions, including orphan drugs. From this the average success rate of Ph 3 for an orphan drug was found to be 66% (if you exclude oncology which has an unusually low success rate).
This means, based upon historical averages, its more likely than not that NNZ-2591 will be approved for both PMS & PHS.
As I've argued previously, based upon the results we've seen for NNZ-2591 (low side-effect profile, improvements across a majority of participants and high statistical significance) NNZ-2591 appears to be better than the average drug.
This suggests to me the likelihood of success in Ph 3 for PMS & PHS will be considerably higher than the 66% historical average.
Additionally, the fact that NNZ-2591 has been successful in 3 independent trials also strengthens the case enormously. A good result in a small Phase 2 study could be well be just good luck - a statistical anomaly - you have to be careful not to extrapolate the results of a small short term trial too far. However, if you're run three independent trials and got similar positive results in all this greatly reduces the chances the result was a 'fluke'. It hugely strengthens your conviction the measured drug effects are real and likely to be replicated in a larger trial.
I think its hard to overemphasise how Neuren's achievement of successful results for the same drug in 3 completely separate trials is. Even if the general public don't appreciate this, I can guarantee the statisticians employed at large Pharma doing their business modelling will understand this - and it will have a major effect on their risk modelling (in the upwards directly).
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