Background: Hydroxychloroquine (HCQ) is an oral drug prescribed to pregnant women with rheumatic disease to reduce disease activity and prevent flares. Physiologic changes during pregnancy may substantially alter drug pharmacokinetics (PK). However, the effect of pregnancy on HCQ disposition and potential need for dose adjustment remains virtually unknown. Methods:We performed a population PK analysis using samples from the Duke Autoimmunity in Pregnancy Registry from 2013-2016. We measured HCQ concentration using HPLC-MS/MS and analyzed data using nonlinear mixed effect modeling. We calculated differences between pregnancy and postpartum Empirical Bayesian Estimates (EBEs) using paired t-tests. We computed steady state concentration profiles for HCQ during pregnancy and postpartum using individual clinical data and EBEs developed from the final PK model.Results: 145 serum samples were obtained from 50 patients, 25 of whom had paired pregnancy and postpartum specimens. Five subjects had average concentrations (pregnancy and postpartum) <100 ng/mL, consistent with medication non-adherence, and were excluded. The population estimated apparent volume of distribution (V d /F) was 1850 L/70kg and estimated apparent clearance (CL/F) was 51 L/hr. Compared with postpartum, median V d /F increased significantly

Almost half of recent pediatric trials failed to achieve labeling indications, due in large part to inadequate study design. Therefore, innovative study methods are crucial to optimize trial design while also reducing the potential harms inherent with drug investigation. Several methods exist to optimize the amount of pharmacokinetic (PK) data collected from the smallest possible volume and with the fewest number of procedures, including the use of opportunistic and sparse sampling, alternative and non-invasive matrices, and micro-volume assays. In addition, large research networks using master protocols promote collaboration, reduce regulatory burden, and increase trial efficiency for both early- and late-phase trials. Large pragmatic trials that leverage electronic health records can capitalize on central management strategies to reduce costs, enroll patients with rare diseases on a large scale, and augment study generalizability. Further, trial efficiency and safety can be optimized through Bayesian adaptive techniques that permit planned protocol changes based on analyses of prior and accumulated data. In addition to these trial design features, advances in modeling and simulation have paved the way for systems-based and physiologically-based models that individualize pediatric dosing recommendations and support drug approval. Lastly, given the low prevalence of many pediatric diseases, collecting de-identified genetic and clinical data on a large scale is a potentially transformative way to augment clinical pharmacology research in children.