Murskautuminen

20260216 For future reference: https://www.nature.com/articles/d41586-025-03967-9

Seemed relevant in this thread.

I agree with your points overall. Regarding "what is a journal except for a complicated algorithm for performing reviews"? I think one point is that there is a hard-to-quantify social contract between the journal editors and specialized reviewers, which are partially hand-selected over many years (and opaque). Editors overall do rely on verifiable experts in the field with an established reputation, both publically and privately. Reviewers can have some sort of direct interaction with the editors, coming with opaque trust-verification. Editors also tend to go to scientific meetings as well and do have undocumented or unofficial interactions with scientists (and their favorite reviewers). Now, reviewers might ask their students to review a paper and they sign off on that after a quick skim, but not all of them do and especially if the paper has more caliber/weight to it, they do tend to take it seriously personally.

Another issue when going to a decentralized tool is that I think it should apply some sort of gate-keeping to only allow academics or verified scientists to contribute reviews, but then you also need a way to prevent bias/friend/self-citation network interactions between the academic reviewers, which means you would need to keep good track of them? Not sure how to handle that.

I think for smaller software papers or ML learning on arxiv this might work. For larger papers on biomedical or hard-tech, I think it is much less likely. I struggle to keep up with BioRxiv.org as a medical professional, many articles would require 2 hours+ to confidently review as a professional and I would never trust a "public" review algorithm or network to necessarily do a great job. If you allow weekly updates on your topic area, you might get a 100 papers a week of which 90 are likely poor-quality, but who is going to review these? Definitely not me, I cannot judge a 100 papers a week. Granted, probably only 1 or 2 are directly relevant to your work, but even then the time sink is annoying. It is nice if a publisher has done some initial quality check, made sure the written text is succinct, direct and validated and backed up by well-presented data/figures/methods. Even if a totally open social network exists for upvoting/describing papers, I am afraid the need for these publishers will still be there and they will just exist regardless, and it will still be preferred by academics.

Three~five experts specifically asked to review a paper in a controlled environment versus a thousands random scientists or public people (which might be motivated by financial, malicious or other reasons) is probably still the better option. Larger, technically impressive multi-disciplinary papers with 20+ authors are basically impossible to review as individuals, you would like a few experts on the main methods to review it together in harmony with oversight from an reputable vendor/publisher. Such papers are also increasingly common in any biotech/hard-tech field.

I am afraid that gatekeeping is partially essential and somewhat desired, as an academic you don't have time to read everything and some sort of quick signals, albeit very flawed, can be useful to stop wasting time reading crappy science. If you don't gatekeep you will get a lot of crappy papers or papers that mention the same thing and it will waste more time from people that wish to get a quick sense of the state of a topic/field from quality work. An open source voting system would be easily abused, so it will end up to be trusting a select service of peer reviewers or agencies. Especially if a paper includes a lot of experiments and figures that can be somewhat complicated or overwhelming. What do think?

Thanks for sharing a great article. Overall, I think the path to develop a drug is very multi-dimensional, cost and time intensive and very slow. Some other elements to add to the article:

- The preclinical drug discovery phase was not mentioned much. This phase involves discovering/designing/creating an actual drug against a potential biological mechanism/target uncovered by biology/genomics. Although this is generally seen as a more tractable element of the drug development path, it can still be very difficult and requires many years of additional research. Even so, some targets are well known to have great potential in disease, but it has been very difficult to generate a selective and potent drug against it. This phase typically does involve more "theoretically defined" research (although it is still messy) with chemists, biophysicists and pharmacologists, which fit more into the article's mention of the "mathematician". Yet, in line with the article, these often-talented people cannot always create a suitable drug for a given target. Providing further evidence that it is not "just" a lack of mathematically talented individuals.

- The reasons for drug failures in the clinic can be more numerous than alluded to in the article. Some drugs are very toxic, not only because of off-target side effects, but potentially also due to on-target side effects. In deadly cancers, this might not be so much of a problem, but it can still be a limiting element when we combine drugs to limit the surfacing of drug resistant cancer cells.

- As mentioned by others, cancer cells are cells from our own body, and they utilize our body's functions in excessive or highly altered manners to grow. However, blocking these functions selectively in cancer cells can be difficult (especially if non-genetically) as these functions often are still present in all other cells.

- Cancer metastasizes, cancer cells can spread across the body and generate new tumors elsewhere in the body. This can be almost anywhere, and it can be very difficult to detect early metastases in a patient. Hence, stopping treatment too early, even though the doctors might not see any cancer cells and the treatment has strong side effects, means you could redevelop cancer. Moreover, some metastatic sites might be in locations that are hard to reach for a given drug, hence they might not be fully targeted by a given drug.

- Full-blown cancers typically do not develop solely because of a single driving genetic alteration. Instead, a series of 2~5 genetic/biological alterations from a potential pool of dozens of genetic factors in combination leads to an aggressive tumor. Note though that it can be true that a single genetic alteration is dominant and drives a large part of cancer growth. Even in a single cancer type (e.g. colon cancer) the combination of 2-5 alterations leading to aggressive cancer can be different. Moreover, even within a single patient, different metastatic sites might evolve on their own and acquire different combinations of these driving factors. Hence, to truly treat some cancers targeting multiple drivers would be ideal, and each patient might require a relatively unique approach.

- Cancer cells are genetically unstable and can rapidly alter their genetic makeup. The DNA of normal human cells consists of two sets of 23 chromosomes that are well-organized and add up to ~3 billion DNA base pairs (the code of life). Cancers show very variable chromosome numbers and some advanced cancers can have more than 100 chromosomes. Moreover, cancer chromosomes can be heavily altered, where pieces of other chromosomes integrate into others, translocate, bridge, reconnect. It can be a total soup of >10 billion DNA base pairs. Moreover, these changes are different for each cancer, so every cancer patient will be more or less unique. This genetic instability also allows cancer cells to rapidly mutate and adapt/develop resistance to a given drug treatment.

Actually, the term opperhoofd was used by chief ranking officers of the Dutch trading companies in Japan. https://en.wikipedia.org/wiki/VOC_opperhoofden_in_Japan

Some products also only make money after a long time of R&D. If you are a startup focused on such a product, R&D tax benefits are critical to keep you alive during your long development time that will require multiple rounds of investment before you can even think of making a profit.

For example, a new drug can take 10~16 years to get to the market, and this is intended by government regulations (there is often no way around it). One has to show the drug is safe and works against the intended disease, before one can sell a drug.

However, before you can even start this clinical work in humans, you must discover the drug first and optimize it through laboratory and animal testing. Only after that can you enter the clinical stage to test it in humans. Clinical stages are typically divided into phase 1/2/3 and each stage requires more time and money. After you complete all that, you can finally ask for approval to sell your drug at a profit from the government (US Food and Drug Administration (FDA)/ EU European medicine agency (EMA)).

This trajectory can typically be divided as follows: Drug Discovery (early lab testing) 2-4 years, $2-15M Preclinical Drug Development (advanced lab/animal testing) 2~3 years, $10-15M Clinical Stage 1 (First in human studies) 1~2 years $15~30M Clinical Stage 2 (medium-sized human study), 2~3 years, $40~60M Clinical Stage 3 (large, longer term human study), 2~4 years $100~300M.

Total: 9~16 years of research & development with a cost of $167~420M. Then finally you can sell the drug for a profit. If you are a startup focused solely on such development, you have no way (or very limited ways) to make money/profit in those 9-16 R&D years. In such a case, R&D tax benefits are really useful.