  Phase I dose-escalation trials must be guided by a safety model in order to
avoid exposing patients to unacceptably high risk of toxicities. Traditionally,
these trials are based on one type of schedule. In more recent practice,
however, there is often a need to consider more than one schedule, which means
that in addition to the dose itself, the schedule needs to be varied in the
trial. Hence, the aim is finding an acceptable dose-schedule combination.
However, most established methods for dose-escalation trials are designed to
escalate the dose only and ad-hoc choices must be made to adapt these to the
more complicated setting of finding an acceptable dose-schedule combination. In
this paper, we introduce a Bayesian time-to-event model which takes explicitly
the dose amount and schedule into account through the use of pharmacokinetic
principles. The model uses a time-varying exposure measure to account for the
risk of a dose-limiting toxicity over time. The dose-schedule decisions are
informed by an escalation with overdose control criterion. The model is
formulated using interpretable parameters which facilitates the specification
of priors. In a simulation study, we compared the proposed method with an
existing method. The simulation study demonstrates that the proposed method
yields similar or better results compared to an existing method in terms of
recommending acceptable dose-schedule combinations, yet reduces the number of
patients enrolled in most of scenarios. The \texttt{R} and \texttt{Stan} code
to implement the proposed method is publicly available from Github
(\url{https://github.com/gunhanb/TITEPK_code}).
