Are t-tests good choices here? I would like to test if two groups of participants (dark-eyed and light-eyed individuals)  respond to a drug differently over time. The hypothesis is that the drug reaches its peak effects sooner but diminishes quicker in light-eyed individuals. Specifically, the following hypotheses will be tested:

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*light-eyed individuals have on average a higher biomarker level for the drug 1 hour after receiving the drug.

*light-eyed individuals have on average a higher biomarker level for the drug 2 hours after receiving the drug.

*light-eyed individuals have on average a lower biomarker level for the drug 4 hours after receiving the drug.

*light-eyed individuals have on average a lower biomarker level for the drug 8 hours after receiving the drug (the primary hypothesis).

*light-eyed individuals have on average a lower biomarker level for the drug 24 hours after receiving the drug.

Are doing 5 individual t-tests appropriate for the hypotheses above? Assuming there is no significant outlier, the sample mean will be a good summary stat to test if the drug levels in both groups are statistically significantly different at a specific time point.
For the normality assumption, the sample size will be 23 for each group (based on a power analysis using the 8 hours mark), so the CLT will likely kick in. I will check the histograms to make sure after the data is collected. The independence assumption will also hold since the measurement for one participant doesn't affect the other.
I am aware of the dependence between the hypotheses - if the drug level is lower at 4 hours for the light-eyed group, then it's likely to be also lower at 8 hours. Therefore, I will do a Bonferroni correction to adjust the alpha level.
Are there any non-parametric alternatives that would be better suited to testing those hypotheses?

I'm not sure if these tests will answer your hypothesis.
It is correct to perform T-tests at the specific time points to check the drug concentrations, however we must remember that different concentrations doesn't always mean different efficacy; you should probably try to expand the sample size and observe also clinical efficacy endpoints
