A Toumazi1, E Comets2, C Alberti3, T Friede4, F Lentz5, N Stallard6, S Zohar1, M Ursino7. 1. INSERM, UMRS 1138, Team 22, CRC, University Paris 5, University Paris 6, Paris, France. 2. INSERM, CIC 1414, University Rennes-1, Rennes, France; INSERM, IAME UMR 1137, University Paris Diderot, Paris, France. 3. INSERM, UMR 1123, Hôpital Robert-Debré, APHP, University Paris 7, Paris, France. 4. Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany. 5. Federal Institute for Drugs and Medical Devices, Bonn, Germany. 6. Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, The University of Warwick, UK. 7. INSERM, UMRS 1138, Team 22, CRC, University Paris 5, University Paris 6, Paris, France. Electronic address: moreno.ursino@inserm.fr.
Abstract
BACKGROUND AND OBJECTIVE: Dose-finding, aiming at finding the maximum tolerated dose, and pharmacokinetics studies are the first in human studies in the development process of a new pharmacological treatment. In the literature, to date only few attempts have been made to combine pharmacokinetics and dose-finding and to our knowledge no software implementation is generally available. In previous papers, we proposed several Bayesian adaptive pharmacokinetics-based dose-finding designs in small populations. The objective of this work is to implement these dose-finding methods in an R package, called dfpk. METHODS: All methods were developed in a sequential Bayesian setting and Bayesian parameter estimation is carried out using the rstan package. All available pharmacokinetics and toxicity data are used to suggest the dose of the next cohort with a constraint regarding the probability of toxicity. Stopping rules are also considered for each method. The ggplot2 package is used to create summary plots of toxicities or concentration curves. RESULTS: For all implemented methods, dfpk provides a function (nextDose) to estimate the probability of efficacy and to suggest the dose to give to the next cohort, and a function to run trial simulations to design a trial (nsim). The sim.data function generates at each dose the toxicity value related to a pharmacokinetic measure of exposure, the AUC, with an underlying pharmacokinetic one compartmental model with linear absorption. It is included as an example since similar data-frames can be generated directly by the user and passed to nsim. CONCLUSION: The developed user-friendly R package dfpk, available on the CRAN repository, supports the design of innovative dose-finding studies using PK information.
BACKGROUND AND OBJECTIVE: Dose-finding, aiming at finding the maximum tolerated dose, and pharmacokinetics studies are the first in human studies in the development process of a new pharmacological treatment. In the literature, to date only few attempts have been made to combine pharmacokinetics and dose-finding and to our knowledge no software implementation is generally available. In previous papers, we proposed several Bayesian adaptive pharmacokinetics-based dose-finding designs in small populations. The objective of this work is to implement these dose-finding methods in an R package, called dfpk. METHODS: All methods were developed in a sequential Bayesian setting and Bayesian parameter estimation is carried out using the rstan package. All available pharmacokinetics and toxicity data are used to suggest the dose of the next cohort with a constraint regarding the probability of toxicity. Stopping rules are also considered for each method. The ggplot2 package is used to create summary plots of toxicities or concentration curves. RESULTS: For all implemented methods, dfpk provides a function (nextDose) to estimate the probability of efficacy and to suggest the dose to give to the next cohort, and a function to run trial simulations to design a trial (nsim). The sim.data function generates at each dose the toxicity value related to a pharmacokinetic measure of exposure, the AUC, with an underlying pharmacokinetic one compartmental model with linear absorption. It is included as an example since similar data-frames can be generated directly by the user and passed to nsim. CONCLUSION: The developed user-friendly R package dfpk, available on the CRAN repository, supports the design of innovative dose-finding studies using PK information.
Authors: Tim Friede; Martin Posch; Sarah Zohar; Corinne Alberti; Norbert Benda; Emmanuelle Comets; Simon Day; Alex Dmitrienko; Alexandra Graf; Burak Kürsad Günhan; Siew Wan Hee; Frederike Lentz; Jason Madan; Frank Miller; Thomas Ondra; Michael Pearce; Christian Röver; Artemis Toumazi; Steffen Unkel; Moreno Ursino; Gernot Wassmer; Nigel Stallard Journal: Orphanet J Rare Dis Date: 2018-10-25 Impact factor: 4.123