The treatment structure of this experiment does not fit tidily into common ANOVA designs. I'll speculate below, but you are more likely to get useful responses from the community if you provide adequate detail about your experimental protocol, something like a Methods section.
You have 35 combinations of peptides (7: A, B, C, D, E, F, Control) x concentrations (5: 6.25,12.5,25,50,100) plus one non-peptide control (a full factorial plus control; Google it). I'm going to assume that time is a repeated measure on whatever your experimental unit is, presumably, a unit within a block.
I don't see that any of your fixed effects are nested within any other fixed effect.
What is comprises a block? Does each block contain all 36 treatment combinations? What is the role of the non-peptide control (in other words, intuitively, what sort of comparisons would you make between the non-peptide control and the other 35 treatments)?
There's not really enough to go on here, and your experimental design might be incompatible with some of these approaches, but I would consider the following. (I'm ignoring the time factor.)
(1) Fit a "means model" with 36 treatments and use contrast statements to test hypotheses of interest.
(2) Fit 7 x 5 factorial (peptide x concentration) treatment structure, dropping the non-peptide control.
(3) Use the non-peptide control as a "baseline" covariate in an analysis of covariance model with a 7 x 5 factorial treatment structure.
(4) Subtract the non-peptide control value from the value of each treatment combination (or divide the value of each treatment combination by the value of the non-peptide control) in each block, and use the result as the response in a model with a 7 x 5 factorial treatment structure. This approach makes strong assumptions about the relationship between the non-peptide control and the peptide treatments that you should be aware of before you implement it.
I sense that you are not experienced with determining either an appropriate structure for an input data set or deterrmining an appropriate statistical analysis. You likely need more assistance than this forum can offer. If there is someone at your company/institution that can help you, seek them out! Do not overestimate what you know and underestimate the value of statistical consultation.
An extremely useful resource is
https://www.sas.com/store/books/categories/usage-and-reference/sas-for-mixed-models-second-edition/prodBK_59882_en.html
Time spent reading this text is not time wasted.
Good luck!
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