Improving Pediatric Protein Binding Estimates: An Evaluation of α1-Acid Glycoprotein Maturation in Healthy and Infected Subjects

Abstract

Background: Differences in plasma protein levels observed between children and adults can alter the extent of xenobiotic binding in plasma, resulting in divergent patterns of exposure.

Objective: This study aims to quantify the ontogeny of α1-acid glycoprotein in both healthy and infected subjects.

Methods: Data pertaining to α1-acid glycoprotein from healthy subjects were compiled over 26 different publications. For subjects diagnosed or suspected of infection, α1-acid glycoprotein levels were obtained from 214 individuals acquired over three clinical investigations. The analysis evaluated the use of linear, power, exponential, log-linear, and sigmoid E _max models to describe the ontogeny of α1-acid glycoprotein. Utility of the derived ontogeny equation for estimation of pediatric fraction unbound was evaluated using average-fold error and absolute average-fold error as measures of bias and precision, respectively. A comparison to fraction unbound estimates derived using a previously proposed linear equation was also instituted.

Results: The sigmoid E _max model provided the comparatively best depiction of α1-acid glycoprotein ontogeny in both healthy and infected subjects. Despite median α1-acid glycoprotein levels in infected subjects being more than two-fold greater than those observed in healthy subjects, a similar ontogeny pattern was observed when levels were normalized toward adult levels. For estimation of pediatric fraction unbound, the α1-acid glycoprotein ontogeny equation derived from this work (average fold error 0.99; absolute average fold error 1.24) provided a superior predictive performance in comparison to the previous equation (average fold error 0.74; absolute average fold error 1.45).

Conclusion: The current investigation depicts a proficient modality for estimation of protein binding in pediatrics and will, therefore, aid in reducing uncertainty associated with pediatric pharmacokinetic predictions.

Figure 1

Ontogeny of AAG among healthy subjects. Concentrations, normalized to CRM470 values, are depicted using estimated geometric mean values (o) for each study group. Geometric error bars depict the log-normal SE associated with each study cohort. Predicted AAG concentrations based on a sigmoid Emax model (solid line - red), as derived from this work, and a linear model (dashed line - blue), as proposed by McNamara and Alcorn (assuming adult plasma AAG concentrations ≈ 93.17 mg/dL) [2], are denoted. Observed data were compiled from the following publications: [, , , , , –49].

Figure 2

AAG ontogeny with respect to (A) PNA and (B) PMA in subjects diagnosed or suspected of infection. Median (i.e. geometric mean) AAG concentrations (solid lines) and associated 95% CI (dashed lines) as estimated using a sigmoid Emax model are depicted. Subjects from the each clinical trial (Staph Trio, □; PTN POPS ●;CLIN01 Δ) are denoted separately.

Figure 3

(A) Comparison of median (geometric mean) AAG concentrations with respect to PNA in healthy (blue dotted line) and infected subjects (Median-red solid line; 95% CI – red dashed line), as estimated by separate sigmoid Emax models. (B) Comparison of normalized estimates of AAG concentrations (i.e. normalized to adult AAG values) with respect to PNA in healthy (blue dotted line) and infected subjects (Median- red solid line; 95% CI –red dashed line). AAG estimates are depicted for postnatal ages ranging between 5 days and 20.5 years.

Figure 4

Predictive performance of McNamara and Alcorn’s (linear) equation vs. the sigmoid Emax equation derived from this analysis at estimating observed pediatric fraction unbound (f_up; n=17) values. Solid and dashed lines depict the lines of best fit (i.e. linear regression) for estimates derived from the sigmoid Emax and linear equations, respectively. The line of identity (dotted) is superimposed for reference.