Help with statistical "robustness" in an experimental study

Hey guys!

im a medical student and i have a problem with a statistcal analysis.

In our experimental design we measure many parameters from an experiment on multiple cell lines. These cells are incubated with different substances and compared to a control group which gets a solvent control.

For statistcal signifcance we use students t-test to measure the p-value within a cell line substance vs control.
The same we do for the other cell line. So we have for each parameter a p-value.
In the next step we want to list every parameter that has the same effect on each cell line after incubation vs control and want to sort these parameters via a "robustness" value or maybe another p-value which contains BOTH values from each cell line.

So basically what i thought we could do is: merge both matrices from the "subpopulations" (cell line a & b substances) and the both "subpopulations" (a & b control) to a new population, and then check for statistical significance via t-test. The main problem is then, we dont have the same amount of samples in each cell line. So we would have in our new merged popoulation an overweight of a cell line with more samples.
If the problem is not clear, i will try to describe it visually, just let me know.

On the other hand we thought about just defining a "robustness" factor which mathematically describes, based on both t-tests of each cell lines within, the robustness of the effect on the parameter.

I just wanted to ask if theres any statistical test we could use to do this or if theres any experience with such kind of problem.

Thank your for your help!