AIMSThe aims of this study were to develop a population pharmacokinetic (PK) model of ampicillin and sulbactam, to identify patient characteristics influencing the PK, and to explore the relationship between dose regimen and degree of renal impairment with exposure and time above minimum inhibitory concentration (MIC). METHODSThis analysis was performed on PK data for ampicillin and sulbactam and MIC data from a clinical trial in Japanese patients with community acquired pneumonia. Simulations were performed to investigate the effects of different dosing intervals on exposure and time above MIC in various degrees of renal impairment. RESULTSThe plasma concentrations from 47 patients were adequately described by a two compartment model with simultaneous fit of ampicillin and sulbactam PK data, where creatinine clearance on clearance and body weight on volume in the peripheral compartment were identified as covariates for both drugs. Creatinine clearance contributed to reducing inter-individual variability of clearance by 16%. Mean clearance (inter-individual variability) for ampicillin and sulbactam was estimated to be 10.7 l h −1 (14.8%) and 10.4 l h −1 (15.2%), respectively. The time above MIC for each pathogen was generally > 50% of the treatment period. Simulations for exposure and time above MIC supported currently recommended dose adjustments. CONCLUSIONSThis study provided a PK model for ampicillin and sulbactam, the time above MICs for identified pathogens and associated simulation results. These findings provide useful information and augment evidence for the established dosage regimens in patients with various degrees of renal impairment.

The current work integrates cell-cycle dynamics occurring in the bone marrow compartment as a key element in the structure of a semimechanistic pharmacokinetic/pharmacodynamic model for neutropenic effects, aiming to describe, with the same set of system-and drug-related parameters, longitudinal data of neutropenia gathered after the administration of the anticancer drug diflomotecan (9,10-difluoro-homocamptothecin) under different dosing schedules to patients (n 5 111) with advanced solid tumors. To achieve such an objective, the general framework of the neutropenia models was expanded, including one additional physiologic process resembling cell cycle dynamics. The main assumptions of the proposed model are as follows: within the stem cell compartment, proliferative and quiescent cells coexist, and only cells in the proliferative condition are sensitive to drug effects and capable of following the maturation chain. Cell cycle dynamics were characterized by two new parameters, F Prol (the fraction of proliferative [Prol] cells that enters into the maturation chain) and k cycle (first-order rate constant governing cell cycle dynamics within the stem cell compartment). Both model parameters were identifiable as indicated by the results from a bootstrap analysis, and their estimates were supported by date from the literature. The estimates of F Prol and k cycle were 0.58 and 1.94 day 21, respectively. The new model could properly describe the neutropenic effects of diflomotecan after very different dosing scenarios, and can be used to explore the potential impact of dosing schedule dependencies on neutropenia prediction.

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.