Not because cancer studies actually are reproducible, but rather because the reason they are not so often reproducible often comes down to the high complexity of the disease.
You grow your HCC1139 cell line in your lab, with your own supply of reagents, and you might get very different gene expression profiles of a lab in a different country growing HCC1139 cell lines on their own supply of reagents. There's so many subtleties in what is going on underneath in these systems that getting them right is insanely hard.
We don't know all the inputs, all the variables, that influence results. Getting the same result in one lab three times doesn't mean that another lab will get exactly the same results. But until we publish the results, we won't know what does and doesn't reproduce, unless there's that stake in the ground and a concerted effort to spend the highly limited time focused on pushing forward the boundaries of knowledge, we won't know.
So even if a broad range of hundreds of wet lab techniques, across thousands of experimental systems, doesn't always get the same result in different hands, unless we start to push at the amount of detail going on we'll never understand how to start to treat all these things.
Anyway, that's my rant, and I can understand why outsiders are concerned about individual cancer research experiments not reproducing at a very high rate, but I think it needs to be placed in this sort of context, where we only have measurement tools for a tiny fraction of what's going on, and need to still discover all the unknown unknowns.
For example, in-vivo tumor experiments in mice can yield completely different results depending on exactly where the tumor was implanted. E.g. a 'lung cancer mouse model' may have the lung cancer injected just under the skin, also known as subcutaneous tumor models, instead of in the lung! Entirely because it's a lot more efficient + yields more trustable data, but the results are often deeply disconnected from how the tumor would naturally grow + respond to drugs within its host organ.
Pharma companies care very much about off target effects. Molecules get screened against tox targets, and a bad tox readout can be a death sentence for an entire program. And you need to look at the toxicity of major metabolites too.
One of the major value propositions of non small molecule modalities like biologics is specificity, and alternative metabolism pathways; no need to worry about the CYPs.
Another thing they fail to account for is volume of distribution. Does it matter if it hits some receptor only expressed in microglia if it can’t cross the blood brain barrier?
Also the reason why off targets for a lot of FDA approved drugs are unknown is because they were approved in the steampunk industrial era.
To me this whole article reads like an advertisement for a screening assay.
sure! i cover this in the essay, the purpose of this dataset is not just toxicity, but repurposing also
>toxicity of major metabolites
this is planned (and also explicitly mentioned in the article)
>no need to worry about CYP’s
again, this is about more than just toxicity
>volume of distribution
i suppose, but this feels like a strange point to raise. this dataset doesnt account for a lot of things, no biological dataset does
>advertisement
to some degree: it is! but it is also one that is free for academic usage and the only one of its kind accessible to smaller biopharmas