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Clinical failure rates over the decades: yikes

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111–120 of 146 posts

Re: Clinical failure rates over the decades: yikes

#112

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 m…

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 fa…

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

Re: Clinical failure rates over the decades: yikes

#115

I'm actually surprised it isn't going up over time. That is naively what you would expect as the low-hanging fruit is plucked. So the fact that it's been stable is probably a sign that scientific advances are roughly keeping pace with the (presumably) increasing challenge of finding ever more targets for drugs.

I actually do this for a living within pharma now! There's a TON of work that goes into drug discovery before we even call it a program. The odds of success are low, so we put in months of work evaluating a candidate before even have a hunch of a program. Yes, the science advances, previously high-hanging fruits become low-hanging become high-hanging again [1], but the tooling also advances: we now have databases lik…

Out of employable curiosity, is there anything that pharma might need help with in terms of coding, pattern-matching, AI assistance, reproducibility, determinism, software controls, ETL, reliable workflow design, or simple IT advice?

Re: Clinical failure rates over the decades: yikes

#116
post #106

Earlier quoted context omitted.

This is very facile thinking - are there any examples of a-e in the real world (namely, a brand new drug program was spun up to exploit a-e) ? I think not, which is why I asked the question.

What an ignorant comment. Are you not familiar with Mavyret, Vosevi, or Xatmep?

I did ask for examples. These aren't really the sort of thing the article is talking about - they're just recombinations of existing medications. I admitted in another comment that I wasn't thinking about these but instead launching a new program to find a new mechanism (which I still haven't found an example of).

Re: Clinical failure rates over the decades: yikes

#117

Earlier quoted context omitted.

This is very facile thinking - are there any examples of a-e in the real world (namely, a brand new drug program was spun up to exploit a-e) ? I think not, which is why I asked the question.

I dont know why you are being rude about a topic you know nothing about. As some who actually does this for a career, they are largely correct. Dose frequency and side effects especially are major reasons. I would add route of administration and patient convenience as well. Anti-vegf treatments for blindness are a good example if you want to research. They all have basicly the same effect in terms of letters preserve…

Not being rude, and I definitely know about medicine.

I explained in sibling that I understood the Lowe article to be about new research (as a researcher, I don't often think about how hard it is to make a GLP survive the stomach but I do think about how incretin biology works). I guess these VEGF agents you list feel like a very straightforward engineering question (can we make an antibody that targets VEGF, humanize its Fc etc) while Lowe brings up conceptual areas like PCSK9, HMG-CoA reductase.

I still maintain that the 1% better thing is not going to lead to incredible success because rational actors aren't going to change their prescribing patterns each time a new agent gets approved.

Re: Clinical failure rates over the decades: yikes

#118

Earlier quoted context omitted.

> Why isnt it rational? You can spend that $1B to win a new category or that same money to maybe be non-inferior to Keytruda, but maybe fail. I think copycat design does happen, but I mostly notice it when drugs are being developed simultaneously at different firms. Perhaps it happens more beyond this, I'm unsure. Otherwise, it makes more sense to try to find an indication where you are approved and have no competiti…

I don't think you are looking at this from the rational economic perspective. I'm not talking about biosimilars or strict copycats. Completely novel indications are a tiny portion of Pharma development. Lucrative and technologically viable untreated indications are few and far between. Meanwhile, you have huge proven markets for treatments that impact millions of people with corresponding Revenue. If you are skeptica…

Understood, I responded elsewhere that I read the Lowe article as talking about conceptual advances (ACE, HMG CoA), but I realize he's mixing both erstwhile untreated indications as well as biosimilars.

Re: Clinical failure rates over the decades: yikes

#119

Earlier quoted context omitted.

Lots of reasons. The new drug could be cheaper to manufacture, fewer side effects, a full cure in stead of a functional cure.

Cost of manufacture is not a meaningful driver of pricing outside the biological How will your clinical trial of the ostensible full cure work if standard of care is curing people?

A full cure would probably have less side-effects, since a functional cure means a life-long dependence on drugs.

Re: Clinical failure rates over the decades: yikes

#120
Drug discovery and development is hard, unique and non comparable in the engineering world. It has it's own properties, like "the pipeline", I-... phases and a mere 20-year long patent duration (counting from molecule discovery). Imagine if after 20 years of it's inception on the paper the car's price falls 5-fold and you have to develop a new machine.

The failure rate does not take into account the millions of molecules that are tested by various methods of protein folding or binding properties. The 90% remains stable with time because on one side an end product is more difficult to produce, on the other the technology of molecule discovery has improved in the same amount.

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