Due to delayed dose limiting toxicities, conventional approaches based on cycle 1 DLT rarely identified MTD in the phase 1 dose-escalation trials for checkpoint targeting agents. Searching MTD based on DLT beyond cycle 1 brings challenges of efficiency to trial design. The time-to-event continual reassessment method (TITE-CRM) incorporating treatment cycle information via adaptive time-to-event weight addresses the issue of efficiency by allowing flexible patient enrollment for shorter trial duration, incorporating early patient drop-out into the model to save number of patients, and capturing DLT cycle pattern. To incorporate pharmacokinetics/pharmacodynamics predictors for MTD, we extended the one-parameter TITE-CRM model to two parameter Bayesian logistic regression model (TITE-BLRM) to increase flexibility. We included four escalation / de-escalation rules to enhance estimation accuracy. Rstan implementation of Hamiltonian Monte Carlo was used for faster sampling and convergence. Simulation studies were performed to evaluate design performance in terms of safety, accuracy, and efficiency.