Summary The tumor suppressing transcription factor p53 is highly conserved at the protein level and plays a key role in the DNA damage response. One important aspect of p53 regulation is its dynamics in response to DNA damage, which include oscillations. Here, we observe that while the qualitative oscillatory nature of p53 dynamics is conserved across cell lines derived from human, monkey, dog, mouse and rat, the oscillation period is variable. Specifically, rodent cells exhibit rapid p53 oscillations, whereas dog, monkey and human cells show slower oscillations. Computational modeling and experiments identify stronger negative feedback between p53 and MDM2 as the driver of faster oscillations in rodents, suggesting that an oscillation's specific period is a network-level property. In total, our study shows that despite highly conserved signaling, the quantitative features of p53 oscillations can diverge across evolution. We caution that strong amino acid conservation of proteins and transcriptional network similarity do not necessarily imply conservation of time dynamics.

Keywords monoclonal antibody • target mediated drug disposition • pharmacokinetics and pharmacodynamics • pharmacometrics Abstract Predictions for target engagement are often used to guide drug development. In particular, when selecting the recommended phase 2 dose of a drug that is very safe, and where good biomarkers for response may not exist (e.g. in immuno-oncology), a receptor occupancy prediction could even be the main determinant in justifying the approved dose, as was the case for atezolizumab. The underlying assumption in these models is that when the drug binds its target, it disrupts the interaction between the target and its endogenous ligand, thereby disrupting downstream signaling. However, the interaction between the target and its endogenous binding partner is almost never included in the model. In this work, we take a deeper look at the in vivo system where a drug binds to its target and disrupts the target's interaction with an endogenous ligand. We derive two simple steady state inhibition metrics (SSIMs) for the system, which provides intuition for when the competition between drug and endogenous ligand should be taken into account for guiding drug development.