Rotatable Bonds 1: Categorical Sins

In this post we start to look at molecular flexibility as a determinant of oral bioavailability. Too much flexibility, as any medicinal chemist knows, is A Bad Thing and rotatable bonds are most definitely of The Dark Side. Well sort of A Bad Thing because too few rotatable bonds are likely to give you a Carbon Based Curiosity rather than orally-dosed drug. Now a molecule with lots of rotatable bonds needs to be quite large so as to be able to accommodate all those bonds and the Rule of 5 tells us that too much large is also A Bad Thing. So your drug sucks. Is it the size or the bonds?

Bioavailability is A Good Thing because without it you don’t have an oral drug. It is normally given as a percentage and is a composite property that quantifies how well your drug is absorbed from the gut and how adept it is at evading the metabolic guardians of the body who mainly hang out in the liver. So you want to figure out whether the rotatable bonds or the size of the molecule that controls bioavailability. One thing you could do is plot bioavailability against number of rotatable bonds and molecular weight and see which descriptor best fits the measured data. Alternatively you could transform the bioavailability because it is a fraction. If the fit doesn’t look linear you might even try a bit of curve-fitting. Hopefully you’ll agree that these are sensible ways to start. However we need to digress a bit before we can introduce the first of the featured articles and beg your indulgence.

If you work long enough in the pharmaceutical industry you’ll come across some particularly creative forms of data analysis. One very common approach is to categorise continuous data. For example we might classify activity as HIGH (IC50 < 100nM), LOW (IC50 > 1µM) and MEDIUM (everything else). Categorising makes some sense when dealing with an assay with low dynamic range where a significant proportion of the measurements are above or below the limits for quantification but you still have to be careful. However categorising continuous data like this has a dark side because it hides variation. Is variation A Good Thing or A Bad Thing? It depends on your perspective. If you’re trying to flog your favourite molecular descriptor to a sceptical audience, variation is definitely A Bad Thing because it sows the seeds of doubt. However if you’re looking for truth, you won’t know whether you’ve found it if you discard variation. Our advice is to sniff for large rodents if presented with any analysis where continuous data has been treated in this manner. Somebody is probably trying to hide something very aromatic and unpleasant.

In the next post we will review a heavily-cited study of the influence of rotatable bonds on oral bioavailability. Needless to say that analysis applies a significant amount of categorisation to the data. We can barely contain ourselves.