Optimization of clinical dosing schedule to manage neutropenia: learnings from semi-mechanistic modeling simulation approach

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.

Predicting Chemotherapy-Induced Neutropenia and Granulocyte Colony-Stimulating Factor Response Using Model-Based In Vitro to Clinical Translation

The ability to predict the incidence of chemotherapy-induced neutropenia in early drug development can inform risk monitoring and mitigation strategies, as well as decisions on advancing compounds to clinical trials. In this report, a physiological model of granulopoiesis that incorporates the drug's mechanism of action on cell cycle proliferation of bone marrow progenitor cells was extended to include the action of the cytotoxic agents paclitaxel, carboplatin, doxorubicin, and gemcitabine. In vitro bone marrow studies were conducted with each compound, and results were used to determine the model's drug effect parameters. Population simulations were performed to predict the absolute neutrophil count (ANC) and incidence of neutropenia for each compound, which were compared to results reported in the literature. In addition, using the single agent in vitro study results, the model was able to predict ANC time course in response to paclitaxel plus carboplatin in combination, which compared favorably to the results reported in a phase 1 clinical trial of 46 patients (r2 = 0.70). Model simulations were used to compare the relative risk (RR) of neutropenia in patients with high baseline ANCs for five chemotherapeutic regimens: doxorubicin (RR = 0.59), paclitaxel plus carboplatin combination (RR = 0.079), carboplatin (RR = 0.047), paclitaxel (RR = 0.031), and gemcitabine (RR = 0.013). Finally, the model was applied to quantify the reduced incidence of neutropenia with coadministration of pegfilgrastim or filgrastim, for both paclitaxel and the combination of paclitaxel plus carboplatin. The model provides a framework for predicting clinical neutropenia using in vitro bone marrow studies of anticancer agents that may guide drug development decisions.