The objective of this study was to describe the pharmacokinetics (PK) of intravenous phenobarbital in neonates and infants on extracorporeal membrane oxygenation (ECMO) and to provide dosing recommendations in this population. We performed a retrospective single‐center PK study of phenobarbital in neonates and infants on ECMO between January 1, 2014, and December 31, 2018. We developed a population PK model using nonlinear mixed‐effects modeling, performed simulations using the final PK parameters, and determined optimal dosing based on attainment of peak and trough concentrations between 20 and 40 mg/L. We included 35 subjects with a median (range) age and weight of 14 days (1–154 days) and 3.4 kg (1.6–8.1 kg), respectively. A total of 194 samples were included in the analysis. Five children (14%) contributing 30 samples (16%) were supported by continuous venovenous hemodiafiltration (CVVHDF). A 1‐compartment model best described the data. Typical clearance and volume of distribution for a 3.4–kg infant were 0.038 L/h and 3.83 L, respectively. Clearance increased with age and CVVHDF. Although on ECMO, phenobarbital clearance in children on CVVHDF was 6‐fold higher than clearance in children without CVVHDF. In typical subjects, a loading dose of 30 mg/kg/dose followed by maintenance doses of 6–7 mg/kg/day administered as divided doses every 12 hours reached goal concentrations. Age did not impact dosing recommendations. However, higher doses were needed in children on CVVHDF. We strongly recommend therapeutic drug monitoring in children on renal replacement therapy (excluding slow continuous ultrafiltration) while on ECMO.
The effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to develop mathematical models for predicting drug effects. Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling. Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy. Therapeutic targeting of autophagy is currently being evaluated in diverse clinical trials but without the benefit of a control engineering perspective. Using a nonlinear ordinary differential equation (ODE) model that accounts for activating and inhibiting influences among protein and lipid kinases that regulate autophagy (MTORC1, ULK1, AMPK and VPS34) and methods guaranteed to find locally optimal control strategies, we find optimal drug dosing schedules (open-loop controllers) for each of six classes of drugs and drug pairs. Our approach is generalizable to designing monotherapy and multi therapy drug schedules that affect different cell signaling networks of interest.
more » « less- NSF-PAR ID:
- 10153364
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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