No abstract

Positron emission tomography (PET) is an imaging technique that is used to investigate ligand-receptor binding in the living brain and to determine the time course of plasma concentration/receptor occupancy (RO). The purpose of this work was to demonstrate the added value of an adaptive-optimal design for PET scan timings and dose selection over traditional study designs involving fixed or educated selections of timings and doses. A k(on)-k(off) model relating plasma concentration to PET data was applied to generate the simulated data. Optimization was performed on scanning timings and doses using the D-optimality criterion. Optimal designs as applied to scanning timings provided unbiased estimates and improved the accuracy of results relative to those of fixed and educated designs. Optimization of both timings and dose provided improvements in accuracy and precision when the initial dose selection was noninformative regarding the time course of RO. These results indicate that adaptive-optimal designs can provide an efficient experimental design for RO studies using PET, by minimizing the number of subjects required and maximizing information related to the plasma concentration-RO relationship.

Despite wide clinical experience with deferiprone, the optimum dosage in children younger than 6 years remains to be established. This analysis aimed to optimize the design of a prospective clinical study for the evaluation of deferiprone pharmacokinetics in children. A 1-compartment model with first-order oral absorption was used for the purposes of the analysis. Different sampling schemes were evaluated under the assumption of a constrained population size. A sampling scheme with 5 samples per subject was found to be sufficient to ensure accurate characterization of the pharmacokinetics of deferiprone. Whereas the accuracy of parameters estimates was high, precision was slightly reduced because of the small sample size (CV% >30% for Vd/F and KA). Mean AUC ± SD was found to be 33.4 ± 19.2 and 35.6 ± 20.2 mg · h/mL, and mean Cmax ± SD was found to be 10.2 ± 6.1 and 10.9 ± 6.7 mg/L based on sparse and frequent sampling, respectively. The results showed that typical frequent sampling schemes and sample sizes do not warrant accurate model and parameter identifiability. Expectation of the determinant (ED) optimality and simulation-based optimization concepts can be used to support pharmacokinetic bridging studies. Of importance is the accurate estimation of the magnitude of the covariate effects, as they partly determine the dose recommendation for the population of interest.

The purpose of this study was to evaluate the outcome prediction power of classical prognostic factors along with surrogate approximation of genetic signatures (AGS) subtypes in patients affected by localized breast cancer (BC) and treated with postoperative radiotherapy. We retrospectively analyzed 468 consecutive female patients affected by localized BC with complete immunohistochemical and pathological information available. All patients underwent surgery plus radiotherapy. Median follow-up was 59 months (range, 6–132) from the diagnosis. Disease recurrences (DR), local and/or distant, and contralateral breast cancer (CBC) were registered and analyzed in relation to subtypes (luminal A, luminal B, HER-2, and basal), and classical prognostic factors (PFs), namely age, nodal status (N), tumor classification (T), grading (G), estrogen receptors (ER), progesterone receptors and erb-B2 status. Bootstrap technique for variable selection and bootstrap resampling to test selection stability were used. Regarding AGS subtypes, HER-2 and basal were more likely to recur than luminal A and B subtypes, while patients in the basal group were more likely to have CBC. However, considering PFs along with AGS subtypes, the optimal multivariable predictive model for DR consisted of age, T, N, G and ER. A single-variable model including basal subtype resulted again as the optimal predictive model for CBC. In patients bearing localized BC the combination of classical clinical variables age, T, N, G and ER was still confirmed to be the best predictor of DR, while the basal subtype was demonstrated to be significantly and exclusively correlated with CBC.

No abstract