A semi-mechanistic dose-finding design in oncology using pharmacokinetic/pharmacodynamic modeling

While a number of phase I dose-finding designs in oncology exist, the commonly used ones are either algorithmic or empirical model-based. We propose a new framework for modeling the dose–response relationship, by systematically incorporating the pharmacokinetic (PK) data collected in the trial and the hypothesized mechanisms of the drug effects, via dynamic PK/PD modeling, as well as modeling of the relationship between a latent cumulative pharmacologic effect and a binary toxicity outcome. This modeling framework naturally incorporates the information on the impact of dose, schedule and method of administration (e.g., drug formulation and route of administration) on toxicity. The resulting design is an extension of existing designs that make use of pre-specified summary PK information (such as the area under the concentration-time curve [AUC] or maximum serum concentration [C_max]). Our simulation studies show, with moderate departure from the hypothesized mechanisms of the drug action, that the performance of the proposed design on average improves upon those of the common designs, including the continual reassessment method (CRM), Bayesian optimal interval (BOIN) design, modified toxicity probability interval (mTPI) method, and a design called PKLOGIT that models the effect of the AUC on toxicity. In case of considerable departure from the underlying drug effect mechanism, the performance of the design is shown to be comparable with that of the other designs. We illustrate the proposed design by applying it to the setting of a phase I trial of a γ-secretase inhibitor in metastatic or locally advanced solid tumors. We also provide R code to implement the proposed design.