Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies

Abstract

SummaryIn cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials.

Semimechanistic Cell-Cycle Type–Based Pharmacokinetic/Pharmacodynamic Model of Chemotherapy-Induced Neutropenic Effects of Diflomotecan under Different Dosing Schedules

“…Model parameter estimates are shown in Table 1. Estimates showed consistency with those obtained in previous studies (Soto et al, 2011). Figure 2A shows that nadir concentrations from schedule I were well described, whereas nadir levels from schedule II simulated according to parameters obtained from the analysis of the schedule I data were not adequately captured (Fig.…”

“…The model-building process was performed sequentially. First, the empirical Bayes estimates of the individual PK parameters were obtained from a previous published population PK model (Soto et al, 2011), and were incorporated to the data set containing the neutrophil counts information. Given the change in the dosing paradigms from cycle 1, and the fact that data were more sparse in terms of number of patients and measurements, the model-building process was performed using only data from the first treatment cycle.…”

“…Diflomotecan (9,10-difluoro-homocamptothecin) is a homocaptothecin, and its mechanism of antitumoral action is related to the inhibition of topoisomerase I, a nuclear enzyme involved in the replication process. The recommended and maximal tolerated doses as well as its pharmacokinetic and neutropenic profiles under single dosing schedules have been previously reported (Troconiz et al, 2006;Soto et al, 2011), using the reference model for neutropenia (Friberg et al, 2002).…”

“…through different routes and/or different dosing schedules (Soto et al, 2011). In the case of topotecan, the drug-related effect parameter was estimated to be 43% lower after oral administration using a different dosing schedule compared with the intravenous administration (Leger et al, 2004).…”

Population Pharmacodynamic Model for Bayesian Prediction of Myelosuppression Profiles Based on Routine Clinical Data after Gemcitabine and Carboplatin Treatment

“…Several models have been proposed [5][6][7][8] , and these models require data from well-designed pharmacokinetic clinical studies. In some cases, such pharmacokinetic information are taken from a phase I clinical study and combined with the myelosuppression data in patients [26] . Wallin et al [27] developed a Bayesian prediction tool of myelosuppression using a population pharmacokinetic/pharmacodynamic model that requires drug concentration data.…”