A Bayesian time‐to‐event pharmacokinetic model for phase I dose‐escalation trials with multiple schedules

Günhan, Burak Kürsad; Weber, Sebastian; Friede, Tim

doi:10.1002/sim.8703

Cited by 19 publications

(26 citation statements)

References 26 publications

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“…We accounted for all types of DLT while allowing the explicit modeling of the main type of toxicity expected (CRS) with PK/PD. Some dose‐finding methods have been proposed that include PK measures 26,52 . However, as PK/PD measures are analyzed by batch (and therefore are not available in real time) and the PK/PD models are developed after several cohorts of patients, a full sequential dose‐allocation approach can be difficult to perform in practice.…”

Section: Discussionmentioning

confidence: 99%

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens

Gerard

Zohar

Ursino

et al. 2021

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Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance.Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose-finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLT o ) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose-allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD-regimen compared to the recommendation of the dose-allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

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Section: Discussionmentioning

confidence: 99%

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens

Gerard

Zohar

Ursino

et al. 2021

Statistics in Medicine

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“…TITE-PK for sequential phase I trials TITE-PK for simultaneous investigation of multiple schedules in phase I trials were introduced in Günhan et al [7], here we adapt it for sequential investigation of multiple schedules. The time-to-first DLT events are modeled using a time-varying (non-homogeneous) Poisson process.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, Günhan et al [7] proposed an alternative dose-schedule finding method, a Bayesian time-to-event pharmacokinetics model (TITE-PK), which uses pharmacokinetics (PK) principles. Unlike other phase I methods, TITE-PK makes use of an exposure-response model that is often more informative than a standard doseresponse model.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Time-to-event Pharmacokinetic Model for Sequential Phase I Dose-escalation Trials with Multiple Schedules

Günhan

Weber

Seroutou

et al. 2020

Preprint

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Background: Phase I dose-escalation trials constitute the first step in investigating the safety of potentially promising drugs in humans. Conventional methods for phase I dose-escalation trials are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.Methods: Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing dose-escalation trial to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model. Results: In a simulation study, the developed appraoch is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example is publicly available (https://github.com/gunhanb/TITEPK sequential).Conclusion: In sequential phase I dose-escalation trials, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.

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“…However, a recent simulation study showed how integrating PK information into the dose escalation design could enrich the knowledge of the dose-toxicity relationship, thus facilitating better dose recommendations for subsequent trials [37]. Recently, new designs that incorporate PK measurements into the dose escalation process [38] or PK/PD in the final dose-response curve estimation [39] have been proposed. Therefore, in order to maximize the probability of clinical success of alternative therapies such as immunomodulation, all available knowledge regarding PK, PD, and toxicology should be modelled and applied as early in the drug research and development process as possible.…”

Section: Introductionmentioning

confidence: 99%

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

et al. 2021

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The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

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A Bayesian time‐to‐event pharmacokinetic model for phase I dose‐escalation trials with multiple schedules

Cited by 19 publications

References 26 publications

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens

A Bayesian Time-to-event Pharmacokinetic Model for Sequential Phase I Dose-escalation Trials with Multiple Schedules

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

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