title: Modelling PK/QT relationships from Phase I dose-escalation trials for drug combinations and developing quantitative risk assessments of clinically relevant QT prolongations.
abstract: In current industry practice, it is difficult to assess QT effects at potential therapeutic doses based on Phase I dose-escalation trials in oncology due to data scarcity, particularly in combinations trials. In this paper, we propose to use dose-concentration and concentration-QT models jointly to model the exposures and effects of multiple drugs in combination. The fitted models then can be used to make early predictions for QT prolongation to aid choosing recommended dose combinations for further investigation. The models consider potential correlation between concentrations of test drugs and potential drug-drug interactions at PK and QT levels. In addition, this approach allows for the assessment of the probability of QT prolongation exceeding given thresholds of clinical significance. The performance of this approach was examined via simulation under practical scenarios for dose-escalation trials for a combination of two drugs. The simulation results show that invaluable information of QT effects at therapeutic dose combinations can be gained by the proposed approaches. Early detection of dose combinations with substantial QT prolongation is evaluated effectively through the CIs of the predicted peak QT prolongation at each dose combination. Furthermore, the probability of QT prolongation exceeding a certain threshold is also computed to support early detection of safety signals while accounting for uncertainty associated with data from Phase I studies. While the prediction of QT effects is sensitive to the dose escalation process, the sensitivity and limited sample size should be considered when providing support to the decision-making process for further developing certain dose combinations. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Novartis Pharma AG Basel, Basel, Switzerland.
authors.forename: Karen
authors.initials: K
authors.lastname: Sinclair
authors.affiliation: Novartis Pharma AG Basel, Basel, Switzerland.
authors.forename: Els
authors.identifier: 0000-0002-6760-0639
authors.initials: E
authors.lastname: Kinable
authors.affiliation: Novartis Pharma AG Basel, Basel, Switzerland.
authors.forename: Kai
authors.initials: K
authors.lastname: Grosch
authors.affiliation: Celgene Neuchatel, Neuchatel, Switzerland.
authors.forename: Jixian
authors.initials: J
authors.lastname: Wang
mesh_terms: D017321:Clinical Trials, Phase I as Topic / Q000379:methods*; D003198:Computer Simulation; D003657:Decision Making; D004305:Dose-Response Relationship, Drug; D015195:Drug Design; D004347:Drug Interactions; D004359:Drug Therapy, Combination; D006801:Humans; D008133:Long QT Syndrome / Q000139:chemically induced*; D008954:Models, Biological; D018570:Risk Assessment / Q000379:methods*; D018401:Sample Size