Being in the biopharma bubble often comes with a side of eyebrow-raising questions from those outside our world. “Why don’t we have a cure for cancer yet? Is Big Pharma hiding it?” I was asked this exact question one day while having my hair cut.
I am not mocking this hairdresser. She was a reasonably intelligent person asking a question of someone she thought might be able to help her understand. I tried.
I explained that I knew a thousand people who would take to the streets if they thought their Big Pharma employer was actually hiding a cure for “Cancer”. But cancer is a thousand different diseases, and each needs its own drugs, sometimes several.
Finding those treatments is hard. Here’s the thing I failed to get across then: science is uncertain and daunting. In biology, there are few hard truths, and the “rules” are riddled with exceptions. The work we’re doing in drug discovery is defined by the fact that we don’t know how things work ahead of time – we just have to try. As Forrest Gump’s mom said, “You never know what you’re gonna get.”
This is the case for all of science, at both big and small scales. In the computational biology side of drug discovery, we are often asked, “how long will it take to build this target validation / off-target assessment / biomarker development study?” You computational biologists already know the answer: “it depends”, on what the therapeutics approach is, what the available tools are, what the error rate is, etc etc. Even when complete, such projects don’t always provide a clear answer, or even a clear status of “FINISHED”.
This is where computational biology differs from the engineering work that many people are familiar with. A project’s success depends on a ton of things that we don’t know yet. Will this project contain a salted caramel new-target miracle or a cherry-toothpaste toxicity surprise?
Until then, we vet these projects as well as we can ahead of time and do our best to guess what will work, what won’t, and which of these tasty-looking things we should try next.