Availability of Safe and Effective Therapeutic Options to Pregnant and Lactating Individuals Following the United States Food and Drug Administration Pregnancy and Lactation Labeling Rule.

OBJECTIVE: To explore the extent and type of pregnancy and lactation data of newly approved prescription drugs and assess whether the presented recommendations are data-driven, as required by the US Food and Drug Administration Pregnancy and Lactation Labeling Rule implemented in 2015. STUDY DESIGN: In this descriptive analysis, we reviewed pregnancy and lactation data of all new molecular entities approved between 2001 and 2020 in their most updated labeling. Information was collected regarding the pregnancy and lactation risk statements, the source of pregnancy and lactation data, and the design and methods of pregnancy and lactation studies in the labeling. RESULTS: Of the 422 new molecular entities, the key advisory statement for use of 133 (32%) drugs in pregnancy and 194 (46%) drugs in lactation were classified as "against use." Less than 2% of all drugs had a key advisory statement that supported their use during pregnancy or lactation. The sources of data regarding use in pregnancy were studies in human and animals in 46 (11%) and 348 (82%) drugs, respectively. For use during lactation, data included studies in human and animals in 23 (5%) and 251 (59%) drugs, respectively. The key advisory recommendation was consistent with the available human information in 4 (8%) drugs in pregnancy and 3 (13%) drugs in lactation. Prescription drug labeling contains limited data to support informed decision-making for the use of prescription drugs during pregnancy/lactation. Close collaboration among stakeholders is required to enhance the availability of data in this population.

Characterizing and Forecasting Individual Weight Changes in Term Neonates.

OBJECTIVES: To develop a mathematical, semimechanistic model characterizing physiological weight changes in term neonates, identify and quantify key maternal and neonatal factors influencing weight changes, and provide an online tool to forecast individual weight changes during the first week of life. STUDY DESIGN: Longitudinal weight data from 1335 healthy term neonates exclusively breastfed up to 1 week of life were available. A semimechanistic model was developed to characterize weight changes applying nonlinear mixed-effects modeling. Covariate testing was performed by applying a standard stepwise forward selection-backward deletion approach. The developed model was externally evaluated on 300 additional neonates collected in the same center. RESULTS: Weight changes during first week of life were described as a function of a changing net balance between time-dependent rates of weight gain and weight loss. Males had higher birth weights (WT0) than females. Gestational age had a positive effect on WT0 and weight gain rate, whereas mother's age had a positive effect on WT0 and a negative effect on weight gain rate. The developed model showed good predictive performance when externally validated (bias = 0.011%, precision = 0.52%) and was able to accurately forecast individual weight changes up to 1 week with only 3 initial weight measurements (bias = -0.74%, precision = 1.54%). CONCLUSIONS: This semimechanistic model characterizes weight changes in healthy breastfed neonates during first week of life. We provide a user-friendly online tool allowing caregivers to forecast and monitor individual weight changes. We plan to validate this model with data from other centers and expand it with data from preterm neonates.