That's a nice and simple rule-of-thumb. But, it's just a starting point. The more independent moving parts (attributes), the more parameters to estimate, and the larger sample size needed to obtain equal precision as a smaller design. That simple rule of thumb you refer to was developed back in the day (early 1990s) when CBC was limited to just 6 attributes and we only knew about aggregate analysis. So, Rich Johnson, who developed the formula assumed it would be applied to pretty straightforward and clean situations.

With the pharma example, there are moving pieces for the patient characteristics and also the drug characteristics. But, if you can make some simplifications such as linear estimation of price parameters (requires just one beta rather than a separate beta for k-1 price levels, where k is the total number of price levels within the attribute) then you can do quite a bit even with limited sample sizes. Of course, you cannot escape sampling error due to small sample sizes. That will always be a limitation. (Except the obvious case where the total population of interest is just 50 people and you are able to interview 48 of the 50, for example. That sample of 48 respondents is associated with really low sampling error since they represent nearly a complete census.)

I recommend people generate dummy respondent data (essentially random answers) for the MBC design they plan to field and that they estimate aggregate logit models of the type they eventually plan to use when the final data are collected. Then, they can observe whether the logit estimation converges and the standard errors have reasonable (small) size. You can look at standard errors with 1000 people, 500 people, 100 people, 50 people, etc. and see the differences.

Often times with pharma research and doctors we just cannot expect to obtain the same precision that we can shoot for with general consumer samples. But, the decision comes down to whether the data will provide measurably better insights than mere guesses. It usually turns out that even though the confidence levels aren't as tight with studies involving doctors than studies involving beverage consumers, the research is still worth every penny due to the signal relative to noise and the confidence intervals one still may obtain even from what seem to be smallish sample sizes.

So, in short, one could look at rules of thumb from CBC as a starting point (as you mention). But, much better to actually build experimental design plans, generate random data, set up the models you plan to estimate in MBC, and estimate using aggregate logit.