  Oncology drug development starts with a dose escalation phase to find the
maximal tolerable dose (MTD). Dose limiting toxicity (DLT) is the primary
endpoint for dose escalation phase. Traditionally, model-based dose escalation
trial designs recommend a dose for escalation based on an assumed dose-DLT
relationship. Pharmacokinetic (PK) data are often available but are currently
only used by clinical teams in a subjective manner to aid decision making.
Formal incorporation of PK data in dose-escalation models can make the decision
process more efficient and lead to an increase in precision. In this talk we
present a Bayesian joint modeling framework for incorporating PK data in
Oncology dose escalation trials. This framework explores the dose-PK and PK-DLT
relationships jointly for better model informed dose escalation decisions.
Utility of the proposed model is demonstrated through a real-life case study
along with simulation.
