Models can also be knowingly/intentionally incomplete, which necessarily introduces noise that you are not controlling for. Meaning you have to use statistics, and there isn't really a concept of prove or disprove in the true sense of those words.
Maybe very far in the future there will be models of human biology that are as robust as classical physics, but right now there is such a large amount that is not understood, it's simply not feasible. A drug could work for one person and not another for reasons beyond the realistic scope of the original development hypothesis. It requires a probabilistic view to make any sort of statement about the efficacy then.
I suppose you could argue these models are just wrong and thus trivially disproven, but I don't think that's a productive framing. I doubt any biologist or doctor would claim they have anywhere near a complete model of how their specialty works. That doesn't mean a particular model isn't useful or isn't the best we currently have to work with.
Plus maybe the third best model will actually turn out to explain a separate puzzle piece in an eventual better model. Mechanistic models in biology aren't always well done in practice, but it's certainly not binary either.
Maybe very far in the future there will be models of human biology that are as robust as classical physics, but right now there is such a large amount that is not understood, it's simply not feasible. A drug could work for one person and not another for reasons beyond the realistic scope of the original development hypothesis. It requires a probabilistic view to make any sort of statement about the efficacy then.
I suppose you could argue these models are just wrong and thus trivially disproven, but I don't think that's a productive framing. I doubt any biologist or doctor would claim they have anywhere near a complete model of how their specialty works. That doesn't mean a particular model isn't useful or isn't the best we currently have to work with.
Plus maybe the third best model will actually turn out to explain a separate puzzle piece in an eventual better model. Mechanistic models in biology aren't always well done in practice, but it's certainly not binary either.