In Silico Comparison of Maximum Tolerated Dose Determination in a Phase I Dose-Finding Framework: Application to Hematological Toxicity for a Histone Deacetylase Inhibitor Abexinostat, Co-Administered with Free or Liposomal Doxorubicin in Solid Tumors
(English)Manuscript (preprint) (Other academic)
Determination of a maximum tolerated dose (MTD) continues to mainly rely on dose escalation studies using the algorithm-based 3+3 design although it has repeatedly been shown to result in a biased and imprecise MTD. Alternative Bayesian methods, i.e. the Continuous Reassessment Method (CRM), the Escalation with Overdose Control (EWOC), the Bayesian Logistic Regression Model (BLRM), and the modified Toxicity Probability Intervals (mTPI) are increasingly gaining interest for Phase I studies. Here we propose to develop an in silico Clinical Trial Simulation (CTS) framework for multiple comparisons of MTD determination and to highlight the potential benefits for model-based methods. This groundwork was exemplified for a combination therapy in which thrombocytopenia was the most frequent Dose Limiting Toxicity (DLT) in two 3+3 dose escalation trials in solid tumors and in ovarian cancer. The recommended Phase II dose (RP2D) was assessed through simulations from a thrombocytopenic toxicity PKPD model developed using the data of these two trials. Dose finding designs (3+3, CRM, EWOC, BLRM and mTPI) were evaluated for accuracy and precision of the predicted RP2D, percentage of DLTs, proportion of under- and over- dosing patients and dose escalation trajectory. Using this framework, the Bayesian methods were shown to be in better agreement with the reference model-based RP2D and provided an increase of 2 dose levels compared to the 3+3 design approach. Furthermore, they provided a better precision of the RP2D and yielded to more ethical trials. This work is in line with the methodology shift advocated by regulators and academics in phase I oncology studies.
population PKPD, thrombocytopenia, NONMEM, MTD, Bayesian methods, 3+3 algorithm, dose escalation study
Research subject Pharmaceutical Science
IdentifiersURN: urn:nbn:se:uu:diva-233443OAI: oai:DiVA.org:uu-233443DiVA: diva2:752560