Successive quadratic programming (SQP) has been the method of choice for the solution of many nonlinear programming problems in process engineering. However, for the solution of large problems with SQP based codes, the combinatorial complexity associated with active set quadratic programming (QP) methods can be a bottleneck in exploiting the problem structure. In this paper, we examine the merits of incorporating an interior point QP method within an SQP framework. This provides a novel interpretation of popularly used predictor-corrector interior point (IP) methods. The resulting large-scale SQP algorithm, with an interior point QP, also allows us to demonstrate significant computational savings on problems drawn from optimal control and nonlinear model predictive control.