Traditionally, model-based dose-escalation trial designs recommend a dose for escalation based on an assumed dose-toxicity relationship. Pharmacokinetic data are often available but are currently only utilised by clinical teams in a subjective manner to aid decision making if the dose-toxicity model recommendation is felt to be too high. Formal incorporation of pharmacokinetic data in dose-escalation could therefore make the decision process more efficient and lead to an increase in the precision of the resulting recommended dose, as well as decreasing the subjectivity of its use. Such an approach is investigated in the dual-agent setting using a Bayesian design, where historical single-agent data are available to advise the use of pharmacokinetic data in the dual-agent setting. The dose-toxicity and dose-exposure relationships are modelled independently and the outputs combined in the escalation rules. Implementation of stopping rules highlight the practicality of the design. This is demonstrated through an example which is evaluated using simulation.