Precision Dosing for Critically Ill Children

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The drug development process and FDA-approved prescribing generally assume that patients are sufficiently stable and similar enough to justify population-based dosing for a given group that is usually unchanged during therapy. Unfortunately, there is a huge body of evidence that dosing according to this “one size fits all” paradigm results in wide variation in plasma drug concentrations between individuals and even within the same individual over time, all of which can compromise clinical outcomes. Population pharmacokinetic (PK) and pharmacodynamic (PD) models can control for this variability by providing clinicians with tools to adjust doses accordingly, a process that has come to be known as Model-Informed Precision Dosing (MIPD). However, MIPD has been better able to control for inter-individual variation rather than interoccasion variation (IOV) within an individual over time. MIPD methods exist to track IOV in the past, but not to account for possible future IOV. In this project we will address IOV in three novel approaches. Our first aim uses our unique Virtual Pediatric Intensive Care Unit (VPICU) dataset with >400 clinical variables obtained from ~20,000 unstable, critically ill children in our hospital since 2009. We will build recurrent neural networks (RNNs) to predict changes in renal function within individuals, which is relevant to the control of renally excreted drugs. While models exist to predict renal failure, this will be the first application of RNNs to predict creatinine clearance in children. There are >100,000 serum creatinine measurements to validate this work. Our second aim is to account for changing PK-PD in models that cannot be linked to a specific covariate like renal function. To do this, will incorporate stochastic differential equations (SDEs) to capture changes in model parameters over time. Unique to our work, we will apply SDEs in the setting of our long history of non-parametric PK-PD modeling, which makes no assumptions about underlying probability distributions for parameter values in a model and is particularly good at describing and controlling unusual patients, perfect for a critically ill population. We will use >40,000 vancomycin doses and >5,000 plasma concentrations in VPICU to test our algorithms. Our third aim is two-fold. First, we will again use RNNs to predict outcomes of VPICU patients with Staphylococcal bloodstream infections treated with vancomycin. We will compare RNNs that include vancomycin exposure estimated with IOV and without IOV. The second part is to use our in vitro hollow fiber infection model (HFIM) to directly assess the effect of vancomycin IOV on both methicillin-resistant and methicillin-susceptible Staphylococcus aureus in our laboratory. The HFIM can reproduce pediatric PK to measure antibacterial kill and emergence of less susceptible or persister organisms over days to weeks. Our inclusion of IOV in the HFIM is completely novel. We will deliver software tools to clinicians to control IOV and understand the magnitude relevant to outcomes of anti-Staphylococcal therapy.