If you have multiple concentrations within each drug, you could use a factorial design approach with interactions between drugs and concentrations.

You would then need to run post-hoc tests to determine which of the drugs (and concentrations) were effective. Because of the multiple comparisons, you will need to adjust the estimated p values to account for multiple testing (such as Bonferroni, etc.).

Thank you Ariel. But I am still confused about the type of statistical analysis I should use. I know nothing about factorial design approach. I am not a statistician basically so I can only use Already available softwares and programmes. Can you please help with this.

Sonal,

My suggestion to you is to find a local statistician to work with. If this work is important to you, then you need a trained individual to assist you. Statistics is more than pushing buttons on a computer.

Perhaps, you may find individuals here on RG that may be willing to help you with your analyses.

Ariel

 will you give me some details of data to suggest solution further as with present scenario it will be difficult for every one to suggest solution.

Regards

Rafique

Sonal, 

The principle factor determine the experimental design is the uniformity of the experimental units ( animal , plant , ...... ) which you used in the experiment,  if your experimental units uniform then you can chose factorial experiment conduct with completely randomized design ( of course you must do replicates for each treatment ) , if your experimental units have one variation factor ( different age , class , breed ,.... ) you can chose factorial experiment conduct with Randomized Complete Blocks Design .

Number of replicates effective factor on accuracy of experiment .