First off, looking at your posting history, I see that you are working with some challenging models. I highly recommend two resources:
(1) https://www.sas.com/store/books/categories/usage-and-reference/sas-for-mixed-models-second-edition/p... (the first volume of the new edition of which will soon be available, I saw a preprint last month, so excited!), and
(2) https://www.amazon.com/Generalized-Linear-Mixed-Models-Applications/dp/1439815127
and
cultivating a statistician as a close colleague--ideally someone you can physically sit with at a table to discuss your research. These models are difficult and persnickety. It is treacherous (and naive) to assume that you can know enough to do a good job with statistics when your main interest is elsewhere (as it should be if you are not a statistician). This forum is not a substitute for real, problem-specific statistical advice.
You are missing a fundamental understanding of the distinction between fixed and random effects, which studing the references above may remedy; plus this paper http://onlinelibrary.wiley.com/doi/10.2307/1941729/abstract has a great discussion of fixed/random issues.
I am assuming below that observations at time 0h and 16h were obtained on the same bottle. If this is not a correct assumption, then everything below would need adjustment.
Assuming that I understand your design correctly, this is where I would start; it is not necessarily where I would end. If your response variables follow the normal distribution, then you could accomplish a similar model using MIXED, but GLIMMIX is my preferred procedure because it is more flexible.
proc glimmix data=have;
class trt tpt donor bottle;
model y = trt tpt trt*tpt;
random donor
donor*trt
bottle(donor trt)
tpt*donor*trt;
run;
Note that I have added a factor for bottle identification that does not currently exist in your dataset.
Donor is your replicate. As such, it must be a random effects factor and should not be in the MODEL or LSMEANS statement. That should address the NON-EST problem, I think.
BOTTLE is a subsample. If you had balanced data, then I would recommend computing a mean for the variable over the 3 bottles, and then using the mean as the response in a simpler model which drops BOTTLE. You could still try this approach; in practice, unless the unbalance is extreme, I've found it makes little difference.
With only two repeated measurements on the same bottle, the choice of covariance structures is reduced to whether variances are the same at 0h and 16h, or not (i.e., homogenous variances). Compound symmetry CS is equivalent to AR(1); the choice is CS versus CSH.
My experience with GLIMMIX (or MIXED) is that it tends to give me the denomDF that I think are appropriate, but I always sketch out what I expect in advance. Sometimes I specify with the ddf= option, but not often. You want to be very comfortable (and knowledgeable) about the statistical model before you override the default. (But you should not trust the default, by default.)
Assuming that the response (conditional on the predictors) follows a normal distribution, you should get the same result using MIXED as GLIMMIX. The challenge lies in first defining the correct model.
