Building an adaptive dose simulation framework to aid dose and schedule selection.

Establishing a dosing regimen that maximizes clinical benefit and minimizes adverse effects for novel therapeutics is a key objective for drug developers. Finding an optimal dose and schedule can be particularly challenging for compounds with a narrow therapeutic window such as in oncology. Modeling and simulation tools can be valuable to conduct in silico evaluations of various dosing scenarios with the goal to identify those that could minimize toxicities, avoid unscheduled dose interruptions, or minimize premature discontinuations, which all could limit the potential for therapeutic benefit. In this tutorial, we present a stepwise development of an adaptive dose simulation framework that can be used for dose optimization simulations. The tutorial first describes the general workflow, followed by a technical description with basic to advanced practical examples of its implementation in mrgsolve and is concluded with examples on how to use this in decision-making around dose and schedule optimization. The adaptive simulation framework is built with pharmacokinetic, pharmacodynamic (i.e., biomarkers, activity markers, target engagement markers, efficacy markers), and safety models that include evaluations of unexplained interindividual and intraindividual variability and covariate impact, which can be replaced and expanded (e.g., combination setting, comparator setting) with user-defined models. Subsequent adaptive simulations allow investigation of the impact of starting dose, dosing intervals, and event-driven (exposure or effect) dose modifications on any end point. The resulting simulation-derived insights can be used in quantitatively proposing dose and regimens that better balance benefit and adverse effects for further evaluation, aiding dose selection discussions, and designing dose modification recommendations, among others.

Modelling of drug-induced QT-interval prolongation: estimation approaches and translational opportunities.

Safety pharmacology studies are performed to assess whether compounds may provoke severe arrhythmias (e.g. Torsades de Pointes, TdP) and sudden death in man. Although there is strong evidence that drugs inducing TdP in man prolong the QT interval in vivo and block the human ether-a-go-go-related gene (hERG) ion channel in vitro, not all drugs affecting the QT interval or the hERG will induce TdP. Nevertheless, QT-interval prolongation and hERG blockade currently represent the most accepted early risk biomarkers to deselect drugs. An extensive pharmacokinetic/pharmacodynamic (PK/PD) analysis is developed to understand moxifloxacin's-induced effects on the QT interval by comparing the relationship between results of an in vitro patch-clamp model to in vivo models. The frequentist and the fully Bayesian estimation procedures were compared and provided similar performances when the best model selected in NONMEM is subsequently implemented in WinBUGS, which guarantees a straightforward calculation of the probability of QT-interval prolongation greater than 2.5 % (10 ms). The use of the percent threshold to account for the intrinsic differences between species and a new calculation of the probability curve are introduced. The concentration providing the 50 % probability indicates that dogs are more sensitive than humans to QT-interval prolongation. However, based on the drug effect, a clear distinction between species cannot be made. An operational PK/PD model of agonism was used to investigate the relationship between effects on the hERG and QT-interval prolongation in dogs. The proposed analysis contributes to establish a translational relationship that could potentially reduce the need for thorough QT studies.

Population pharmacokinetic model of ibrutinib, a Bruton tyrosine kinase inhibitor, in patients with B cell malignancies.

PURPOSE: Ibrutinib is an oral Bruton's tyrosine kinase inhibitor, recently approved for the treatment of mantle cell lymphoma (MCL) and chronic lymphocytic leukemia (CLL) patients with at least one prior therapy. We developed a population pharmacokinetic (PK) model for ibrutinib in patients. METHODS: Ibrutinib PK data (3,477 observations/245 patients) were available from the following clinical studies: (1) A phase I dose-escalation study in recurrent B cell malignancies (dose levels of 1.25-12.5 mg/kg/day and fixed dose of 560 mg/day); (2) a phase II study in MCL (fixed dose level of 560 mg/day); (3) a phase Ib/II dose-finding study in CLL (fixed dose levels of 420 and 840 mg/day). Different compartmental PK models were explored using nonlinear mixed effects modeling. RESULTS: A two-compartment PK model with sequential zero-first-order absorption and first-order elimination was able to characterize the PK of ibrutinib. The compound was rapidly absorbed, had a high oral plasma clearance (approximately 1,000 L/h) and a high apparent volume of distribution at steady state (approximately 10,000 L). PK parameters were not dependent on dose, study, or clinical indication. The fasting state was characterized by a 67 % relative bioavailability compared with the meal conditions used in the trials and administration after a high-fat meal. Body weight and coadministration of antacids marginally increased volume of distribution and duration of absorption, respectively. CONCLUSIONS: The proposed population PK model was able to describe the plasma concentration-time profiles of ibrutinib across various trials. The linear model indicated that the compound's PK was dose independent and time independent.