Martje Van Neste (1), Julia Macente (1), Anne Smits (1,2), Pieter Annaert (1,3), Karel Allegaert (1,4)
(1) KU Leuven, Belgium (2) University Hospitals Leuven, Belgium (3) BioNotus GCV, Belgium (4) Erasmus University Medical Center, The Netherlands
Introduction: Physiologically-based pharmacokinetic (PBPK) models are a mechanistic model to predict pharmacokinetics (PK) based on study design and medicine- and population-specific parameters. Although the knowledge on PK and PBPK modelling is expanding, there is still limited insight in the physiology of special populations, such as lactating women and breastfed infants. Infant physiology changes rapidly. Related to this fast maturation, differences have been observed between formula- and breastfed infants, including, but not limited to their growth during the first year. These differences are not yet sufficiently described and their impact not yet adequately evaluated in PBPK applications, especially in the setting of lactation-related medicine exposure.
Objectives: Compare age-dependent infant growth-functions, to a manually compiled growth dataset, based on pooled literature data, of exclusively breastfed infants to subsequently assess the sensitivity of PBPK models to these different growth functions.
Methods: The literature was systematically searched on the physiology of exclusively breastfed (at least 4 months) infants, as registered on Open Science Framework (1) In total, 223 articles were included. Data from articles reporting height and weight were extracted and pooled to mean and standard deviations (SD) of longitudinal timepoints during the first year of life. This dataset was compared to different height and weight equations, including Troutman et al. (2018), Chang et al. (2021), National Center for Health Statistics (NCHS) and Simcyp equations (2–5). For height and weight data from the World Health organization (WHO) and PK-Sim (6,7), a polynomial trendline was generated in Microsoft Excel. Similarly, a trendline for the manually compiled breastfed infant dataset was generated and compared to the other height and weight equations and trendlines. To assess the impact of these equations, the primary PK parameters Area Under the Curve (AUC) and Maximum plasma concentration (Cmax) were compared in the predictions of two evaluated PBPK models for midazolam (MDZ; 5 mg dose) and levetiracetam (LEV; 20 mg/kg dose) using the Simcyp, Chang et al. and WHO trendline functions for height and weight (4–6).
Results: Based on visual inspection, the WHO trendlines for height and weight showed best concordance with our dataset for exclusively breastfed infants. PBPK-based simulations with Simcyp for an oral MDZ dose of 5 mg at three months of age yielded AUC values of 3.84, 3.69 and 3.60 mg/L*h, when using the Simcyp default, Chang et al. and WHO growth functions respectively. At 6 months, the predicted AUC’s were 1.70, 1.69 and 1.68 mg/L*h, at 9 months 1.05, 1.08 and 1.10 mg/L*h and at 12 months 0.79, 0.82 and 0.85 mg/L*h, respectively. The predicted Cmax for MDZ was 664, 633 and 616 µg/L at 3 months, 423, 423 and 420 µg/L at 6 months, 315, 326, 331 µg/L at 9 months and 258, 272 and 283 µg/L at 12 months of age. When applying a weight-based dosing of 0.5 mg/kg, the differences were smaller, ranging from 0.1-0.4 mg/L*h for AUC and 1-5 µg/L for Cmax. The same 3 height and weight equations in the LEV PBPK model, after oral administration of 20 mg/kg, generated a predicted AUC of 336, 333 and 329 mg/L*h at 3 months, 311, 309 and 308 mg/L*h at 6 months, 299, 300 and 300 mg/L*h at 9 months and 295, 294 and 294 mg/L*h at 12 months, respectively. The predicted Cmax of LEV was similar for the Simcyp, Chang et al. and WHO equations (28.7 mg/L at 3 months, 29.2 mg/L at 6 months, 29.5 mg/L at 9 months and 29.7 mg/L at 12 months, respectively).
Conclusions: As the latest WHO height and weight data is based on a population of breastfed infants, this equation was expected to be the most similar to our dataset (6). Differences were observed for the AUC and Cmax parameters for 5 mg midazolam, mainly at the ages of 3 and 12 months. Weight-based dosing predictions showed minimal to no differences when comparing the Simcyp default, Chang et al. and WHO height and weight functions (4–6). This review highlights the value of a structured workflow with a systematic search to collect physiological data in breastfed infants to generate mathematical functions, and its comparison to current available functions. Therefore, this methodology is relevant to describe different aspects of infant physiology, such as body composition and clearance, as functions in lactation related PBPK platforms and to assess the impact on simulated PK profiles.
Acknowledgements: This work has received support from the EU/EFPIA Innovative
Medicines Initiative 2 Joint Undertaking ConcePTION grant no. 821520. This paper only reflects the personal views of the stated authors.
References:
[1] Van Neste M, Bogaerts A, Smits A, Annaert P, Allegaert K. A structured review about the physiology of breastfed infants, with a special interest in the effects on drug absorption and transfer: https://osf.io/mhgf2/
[2] Troutman JA, Sullivan MC, Carr GJ, Fisher J. Development of growth equations from longitudinal studies of body weight and height in the full term and preterm neonate: From birth to four years postnatal age. Birth Defects Res. 2018 Jul 3;110(11):916–32.
[3] Centers for Disease Control and Prevention (CDC). 1977 NCHS Growth Chart Equations: https://www.cdc.gov/growthcharts/1977charts.htm
[4] Chang HP, Kim SJ, Wu D, Shah K, Shah DK. Age-Related Changes in Pediatric Physiology: Quantitative Analysis of Organ Weights and Blood Flows: Age-Related Changes in Pediatric Physiology. AAPS Journal. 2021 May 1;23(3).
[5] Simcyp PBPK simulator: https://www.certara.com/software/simcyp-pbpk/
[6] Centers for Disease Control and Prevention (CDC). WHO Growth Charts: https://www.cdc.gov/growthcharts/who/boys_length_weight.htm
[7] Annals of the ICRP. Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values. J. Valentin. Vol. 32 (3-4). 2002.
Reference: PAGE 32 (2024) Abstr 11198 [www.page-meeting.org/?abstract=11198]
Poster: Real-world data (RWD) in pharmacometrics