Changes in R0/R∞ ratio and membrane capacitance are associated with milk removal from the breast

Development of a Generic Physiologically-Based Pharmacokinetic Model for Lactation and Prediction of Maternal and Infant Exposure to Ondansetron via Breast Milk

Ondansetron is commonly used in breastfeeding mothers to treat nausea and vomiting. There is limited information in humans regarding safety of ondansetron exposure to nursing infants and no adequate study looking at ondansetron pharmacokinetics during lactation. We developed a generic physiologically-based pharmacokinetic lactation model for small molecule drugs and applied this model to predict ondansetron transfer into breast milk and characterize infant exposure. Drug-specific model inputs were parameterized using data from the literature. Population-specific inputs were derived from a previously conducted systematic literature review of anatomic and physiologic changes in postpartum women. Model predictions were evaluated using ondansetron plasma and breast milk concentration data collected prospectively from 78 women in the Commonly Used Drugs During Lactation and infant Exposure (CUDDLE) study. The final model predicted breast milk and plasma exposures following a single 4 mg dose of intravenous ondansetron in 1,000 simulated women who were 2 days postpartum. Model predictions showed good agreement with observed data. Breast milk median prediction error (MPE) was 18.4% and median absolute prediction error (MAPE) was 53.0%. Plasma MPE was 32.5% and MAPE was 43.2%. The model-predicted daily and relative infant doses were 0.005 mg/kg/day and 3.0%, respectively. This model adequately predicted ondansetron passage into breast milk. The calculated low relative infant dose indicates that mothers receiving ondansetron can safely breastfeed. The model building blocks and population database are open-source and can be adapted to other drugs.

Staging Breast Cancer-Related Lymphedema with Bioimpedance Spectroscopy

Background: A number of different classification schemes, with generally similar but not identical criteria, exist to describe the different stages of lymphedema. Criteria may include both subjective and objective assessments. The present study investigated whether bioelectrical impedance parameters had utility in staging breast cancer-related lymphedema. Methods and Results: Hierarchical agglomerative cluster analysis was used to assign women (n = 221) at risk of or with clinically ascribed lymphedema to clusters sharing similar impedance characteristics. Five clusters could be identified with each cluster containing proportions of participants that closely aligned with staging allocation, according to International Society of Lymphology criteria, at initial presentation. The use of cluster analysis for tracking of lymphedema progression or response to treatment is demonstrated. Conclusions: No single assessment provides definitive assignment of a patient to lymphedema stage. Staging is usually achieved by identifying and allocating a patient to a lymphedema stage shared by a group of patients with similar clinical signs. Cluster analysis of impedance data provides similar groupings of patients and could provide a useful adjunct objective assessment for staging lymphedema.

Bioimpedance Spectroscopy of the Breast

Background: Bioimpedance spectroscopy (BIS) measurements of breast lymphedema poses practical and technical challenges, in particular the determination of the resistance at zero frequency (R₀), the index of change in breast lymph content. Conventionally, R₀ is calculated from data analysis by using a procedure eponymously known as Cole modeling, a method that is error-prone in the breast. The aim of this study was to evaluate polynomial curve fitting as an alternative analytic procedure. Methods and Results: A sub-set of breast BIS measurements from 41 women with self-ascribed breast lymphedema obtained as part of the Breast Edema Exercise Trial (BEET) were analyzed by both the Cole and polynomial methods. BIS files for all subjects were able to be analyzed by using the polynomial method but only 73% and 88% of data files were analyzed for the affected and unaffected breasts, respectively, by using the Cole method. For those files that were capable of being analyzed by both methods, R₀ values were highly correlated (r = 0.99) but with a small (1.6%) although statistically significant difference (paired t test, p < 0.001) between methods. Conclusions: Analysis of BIS data using polynomial curve fitting is an acceptable and robust alternative to Cole modeling, particularly where impedance measurements are susceptible to technical sources of error of measurement. The small magnitude of difference observed between methods is unlikely to lead to misclassification of patients with lymphedema based on BIS assessment.