Challenges in predicting the pharmacokinetics of drugs in premature and mature newborns: example with piperacillin and tazobactam

24.1

Introduction

Using pharmacokinetic models to predict drug plasma time courses after drug or chemical administration to newborns is an active field of research and application [ ]. Physiologically based pharmacokinetic (PBPK) modeling is one of the more sophisticated computational tools. This type of mathematical modeling has its foundational methodology based on physiology and biochemistry, which is important when considering maternal physiology changes that occur during pregnancy and lactation, and for the growth of the fetus and neonate. Human PBPK models have been constructed for drugs [ ] and chemicals [ , ] during pregnancy and lactation, and in infants and children [ , ].

For pediatrics, PBPK models submitted to the FDA for drug registration were judged to be inadequate in a 2016 publication [ ]. In a 2017 review [ ] of published pediatric PBPK models, the authors discussed the challenges and needs for this field to advance. While limited pharmacokinetic plasma time course data are available for newborns, opportunistic data sets are more common. Variability in drug pharmacokinetic data for newborns, preterm, and term is a recognized challenge [ ].

This chapter reviews the recent research effort to develop a neonate PBPK model, which was described in three manuscripts [ ]. Many of the recent pediatric PBPK models are constructed using scripted commercial software. To carefully evaluate the model code assumptions, we scripted (constructed) our neonate PBPK model using acslX simulation software [ ]. Writing code to create a PBPK model is a common practice in chemical toxicology. In our case, we constructed a fit-for-purpose PBPK model to describe the pharmacokinetics of the combination drug piperacillin (PIP) and tazobactam (TAZ) in preterm and term neonates. The research goal was to determine if the variability associated with neonate maturation (physiology and renal excretion) accounted for the variability observed in plasma concentrations for PIP and TAZ in newborns [ ], both preterm and term neonates. A careful evaluation of the neonate PBPK model that we constructed allowed us to better understand model failures and successes in predicting PIP and TAZ plasma concentrations.

24.2

What is a PBPK model?

PBPK modeling is a cross-disciplinary approach that leverages physiological and biochemical knowledge for estimating the disposition of drugs and chemicals and their metabolites in humans and other animal species. These models are useful in predicting the internal doses of drugs or chemicals at target tissues of concern and in understanding their absorption, distribution, metabolism, and excretion (ADME) profiles in different species of interest. In this approach, the body is conceptually divided into several compartments or tissue groupings consisting of different organs such as the liver, kidney, heart, and brain that are interconnected by arterial and venous blood flows. This framework is then described mathematically using a system of ordinary differential equations that govern the rate of transfer, formation, and accumulation of a drug or chemical and its metabolites in different body tissues. Knowledge about physiological parameters such as organ weights and blood flow rates as well as biochemical parameters such as partition coefficients in body tissues is essential for adequate description of the disposition and fate of a drug or chemical in the body. Equally important is knowledge about clearance mechanisms by which a drug or chemical is eliminated from the body, especially metabolism rates in the liver and glomerular filtration rate (GFR) for the kidneys. A recent review article examines the influence of birth on GFR maturation [ ]. Data from in vitro and animal studies are used when clinical data are not available and suitable approaches such as IVIVE (in vitro to in vivo extrapolation) and allometric scaling are used for extrapolating parameter values across species. Allometric scaling is an empirical approach for extrapolating the pharmacokinetics of an administered chemical or drug across species [ ]. The default application of allometric scaling relies on multiplying model parameters by body surface area or body weight (BW) using a power function of less than 1.0, usually 0.75. Scaled model parameters include biochemical reactions (e.g., metabolism), physiological parameters (e.g., cardiac output), or permeability constants for describing transmembrane movement of drugs or chemicals in organs. Default allometric scaling methods do not usually describe systemic clearance of drugs in children [ ] because during development these simple scaling methods do not reflect the impact of maturation processes. PBPK models allow for simulating “what if” scenarios to better understand the ADME processes. PBPK models can be used for route-to-route extrapolation and provide optimum dosing design for targeted therapeutic levels. For susceptible subpopulations with gaps in data and knowledge, PBPK models provide important in silico pharmacokinetic projections, such as in pregnancy (mother and fetus) and in lactation (mother and nursing neonate or infant).

24.3

Neonates are not just “little adults”

The FDA [ ] defines “the neonatal period for term and postterm newborns as the day of birth plus 27 days, and for the preterm newborn, as the day of birth, through the expected date of delivery, plus 27 days”. Generally, children grow rapidly from birth to about 1 or 2 years during the neonate and infant periods. The dynamic and nonlinear development of children makes them not just “little adults” [ ]. Similarly, neonates are not just “little children.” Compared to children, the differences in physiology of neonates and infants have impacts on the ADME of drugs. For instance, the GFR is at a much lower level in the term infant, increases rapidly, and approaches adult levels by the first year of age [ ]. In addition, important phase 1 drug metabolism enzymes, including CYP1A2, CYP2C9, CYP2D6, and CYP3A4, are substantially lower in neonates, but increase significantly to the adult levels within weeks to 1 or 2 years after birth [ ]. In another study, the age-specific plasma half-lives of 40 substrates in premature and term neonates and in infants up to 2 months of age were greater than corresponding plasma half-lives for adults [ ]. Simply scaling drug doses from adults to children or neonates per BW may lead to overdosing or underdosing of neonates and infants. Also, administration of drug combinations in neonates may indicate that drug–drug interactions (DDIs) are of concern, in addition to differences in physiology. Among hospitalized neonates and infants in US children's hospitals in 2011, over 37% of them were reported to have potential DDIs [ ]. PBPK modeling has been intensively applied for new drug approvals in FDA. Based on 254 Investigational New Drug and New Drug Applications submissions reviewed by FDA's Office of Clinical Pharmacology from 2008 to 2017, 15% included PBPK modeling and simulations for pediatrics and 67% for DDIs [ ]. PBPK modeling is a powerful tool for dose adjustment of neonatal drug treatment based on our current knowledge of ontogeny for physiological developments in neonates. Besides maturation of PK-relevant physiologies in neonates, neonatal diseases may not be the same as those in children or adults, which leads to additional considerations in the dose selection for neonatal drugs [ ].

24.4

PIP and TAZ PBPK model for preterm and term neonates [ ]

24.4.1

Overview

One seven-compartment PBPK neonate model was developed for PIP and another for TAZ ( Fig. 24.1 ). This PBPK model is called fit for purpose, meaning that the construct of the model is limited in its use and is not considered a generic PBPK model. Additional coding is required to consider drugs that are metabolized or administered by routes other than intravenous dosing. Special features for each PBPK model included maturation of the organs or lumped compartments, cardiac output, blood flows, and BW gain. The BW gain was described for five categories of birth age; four groups were preterm (gestation age (GA) 25 (24–26), 28 (27–29), 31 (30–32), and 34 (33–36) weeks) and one group was term (GA ≥37 weeks). The maturation of blood flows and tissue weights was directly or indirectly linked to BW. Renal excretion of PIP and TAZ was described by glomerular filtration and active transport (tubular secretion). In addition, the variability of each model parameter was accounted for using 90% distribution intervals obtained by Monte Carlo simulations. Readers are referred to Yang et al. 2019 [ ] and Fisher et al. 2019 [ ] to find details about model parameters.

PIP and TAZ have a long history of use in adults, children, and newborns to treat infections [ ]. The ADME for drugs and chemicals, in general, are not well studied in detail for neonates because of experimental and clinical limitations and ethical concerns. In this case study of 31 neonates [ ], 3 or 4 blood samples were taken from several neonates after an intravenous dose of PIP and TAZ, which provided important clearance information for these drugs. Here, armed with pharmacokinetic information in older children and adults combined with the neonatal clearance information [ ], we were able to create a PBPK model for the neonate. An inactive minor metabolite of TAZ, called M1, has been reported in adults and was reported intermittently in children with an average age of 3.4 years [ ]. In healthy adults, approximately 20% of the administered TAZ is excreted in urine [ ]. Because of this uncertainty associated with the metabolic pathway in newborn neonates, metabolism was not considered as a clearance pathway for TAZ. Biliary excretion of PIP is another nonrenal clearance process that was not included in the model but has been reported in adult patients [ ].