Another idea is to use a probit analysis with JMP. You measure cell survival, so if you know the number of cells at the start and the number still alive at the end, you can use the linear predictor with these counts.

I do not have an example like your data, but I can illustrate with the Ingots2 data table in the JMP Sample Data folder. Here is the data table:

We have the count of those ingots that are ready, not ready, and the total. This is analogous to your cells that are alive, dead, and total. Next we open the Fit Model dialog and make these changes:

The initial model includes both factors and their interaction. Your factors would be the drugs instead. The results for this model show that the interaction here is not significant.

The insignificant terms are removed and the reduced model (in this case) has only one factor.

We can see that as the factor level increases, the proportion not ready also increases. You can also use a profiler with this model.