[mbnielsen]

Many comments so far seem to try to handwave away the 90 % failure as somehow "optimal" in the system, which seems absurd to me. It is clearly not advantageous for individual companies to keep a drug candidate alive long enough for it to fail in stage III or IV. One obvious question is why they don't and it is very, very tempting to speculate that it's because the problems are getting harder, we are targeting novel mechanisms etc. Again, I think this misses a simpler explanation:

As with many cases where companies make seemingly bad decisions, I think a lot of the explanation lies in system dynamics. Think about the incentive structure inside large pharma companies - it is generally not a career advancement move for a project manager to kill the drug candidate they oversee. It is career advancing to get it approved for the next stage. What could possibly go wrong in this world?

[mft_]

> it is generally not a career advancement move for a project manager to kill the drug candidate they oversee.

You're grossly oversimplifying the process. The decision to "kill" a drug is huge, especially if it's already in the clinic (per the article). That decision will be taken by a large group of people, not an individual - and certainly not a "project manager".

[rwmj]

Sure, but Pharma A is full of career managers who never kill drugs under development and Upstart B relentlessly culls drugs that don't work early on. Upstart B's failure rate at the final stages is under 50% so they develop 5 times as many drugs, beating the existing company, and indeed all other drug companies.

Since this isn't happening, it seems this might not be the explanation.

[ozim]

For me it seems this simplification falls flat right away when you take into account budget constraints.

It is not software development where you can start a project every month see how it goes and drop it or pivot.

I guess they use a lot of computer aided models before they even start serious parts but I believe this discussion is not about failure rates on that stage because then it would be 99%

[logicchains]

The human body is an extremely complex system. Simulating it completely accuracy would require a computer many orders of magnitude more powerful than anything currently existing, so the only way to know if a treatment doesn't produce any unexpected side effect is years of empirical testing, because such things can take years to manifest. Fundamentally the problem space contains inescapable complexity; it's not the fault of pharma firms.

[lnenad]

Not just that, even if we had the power to do so, we don't know anywhere near 100% of our bodies.