Generalized Likelihood Ratios for Designing Dose Optimization Studies of Targeted Therapies

AbstractDose optimization studies of new therapeutic agents aim to identify one or more promising doses for further evaluation in subsequent studies. Traditionally, dose optimization has focused on finding the maximum tolerated dose (MTD), assuming that drug activity and efficacy generally increase with increasing dose. For modern targeted agents, the dose-activity relationship is often non-monotone and such that activity starts to plateau or even decline before reaching the MTD. Finding the optimal biological dose (OBD) for a targeted agent requires considering both toxicity and activity in dose optimization. This article proposes a new design for finding the OBD that utilizes generalized likelihood ratios (GLRs) to measure statistical evidence regarding key scientific questions on toxicity and activity. This GLR-based design requires no parametric modeling assumptions and only assumes that the dose-toxicity relationship is monotone and that the dose-activity relationship follows a two-sided isotonic regression model. Compared with existing designs that operate under similar assumptions, the GLR-based design is more general and more flexible, and performs competitively in simulation experiments where drug activity starts to plateau or decline before reaching the MTD.Key words: dose findingdose transition ruleisotonic regressionlaw of likelihoodmonotonicityoptimal biological doseDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.