Predicting Pharmacokinetics of Pediatric Monoclonal Antibodies: History
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Contributor:
Andrew Lim
,
Pradeep Sharma
,
Oleg Stepanov
,
Venkatesh Pilla Reddy

Ethical regulations and limited paediatric participants are key challenges that contribute to a median delay of 6 years in paediatric mAb approval. To overcome these barriers, modelling and simulation methodologies have been adopted to design optimized paediatric clinical studies and reduce patient burden. The classical modelling approach in paediatric pharmacokinetic studies for regulatory submissions is to apply body weight-based or body surface area-based allometric scaling to adult PK parameters derived from a popPK model to inform the paediatric dosing regimen. However, this approach is limited in its ability to account for the rapidly changing physiology in paediatrics, especially in younger infants. To overcome this limitation, PBPK modelling, which accounts for the ontogeny of key physiological processes in paediatrics, is emerging as an alternative modelling strategy. 

  • pharmacokinetic modelling
  • PBPK modelling
  • popPK modelling
  • pharmacokinetics
  • pediatrics
  • monoclonal antibodies
  • physiologically based pharmacokinetic modelling
  • population pharmacokinetics

1. Classical Modelling Approach for Pediatric Dosing Regimen

Modelling and simulation approaches to extrapolate the first-in-paediatric dose ranged from allometric scaling based on body size to more complex physiologically-based pharmacokinetic (PBPK) and population-pharmacokinetic (pop-PK) modelling [1][2]. The strengths and limitations of these approaches are summarized in Table 1.
The classical approach that pharmacokinetic studies used for regulatory submissions to FDA and EMA were to apply both the body weight-based or body surface area (BSA)-based allometric scaling to adult PK parameters derived from the popPK model for the prediction of PK in children [2][3]. Out of the 39 mAbs currently approved for paediatric usage, only 10 mAbs have PBPK models for human adults or paediatrics.
Allometric scaling from adults to children based on body weight to determine the dosing regimen may be appropriate for mAb, where PK is primarily correlated with body weight and exhibits linear PK [4][5][6][7]. However, when mAb clearance is not scaled linearly with weight, a body-weight dosing approach could result in a clinically sub-optimal dose for children from a lower weight group [5][8]. Hence, in clinical practice, allometric scaling is rarely applied to paediatric dosing for any approved mAbs in isolation but is rather combined with more scientifically rigorous approaches such as population PK (popPK) modelling to determine safe yet effective mAb exposures for the paediatric population [4][5][6][9][10][11][12].
PopPK leverages mathematical models to evaluate pooled PK data from different clinical studies. Covariate information (weight, age or gender) can be integrated into popPK analysis, and this could help explain PK variability within the population [13]. The key advantages of this approach are the ability to analyse sparse data (typical for paediatric studies) and to identify and include the covariates that affect PK, facilitating paediatric dose selection [14]. For instance, body weight is a commonly included covariate in popPK modelling since it was established to significantly impact mAb PK.
Despite popPK modelling being commonly used to support the paediatric clinical trial design [15][16][17][18], its ability to capture the complexities of physiological changes in paediatrics and the impact of mAb on PK is restricted [19]. While age can be included as a sigmoidal maturation function (guided by the sum of gestational and postnatal age) in popPK modelling to explain the maturational differences between paediatrics and adults [14], it is crucial to acknowledge that children are not small adults. There is a growing body of evidence that suggests allometric scaling based on body weight cannot reflect the complex developmental processes that occur during paediatric growth, especially in younger age groups [20]. This is supported by several studies corroborating evidence that the rapidly changing physiology in young children could affect the pharmacokinetics of mAb. For instance, extracellular fluid volume decreases rapidly following birth, whereas plasma volume gradually increases, leading to a higher proportion of the total body volume available for mAb distribution [21][22]. While allometric scaling within popPK modelling can account for size differences between adults and children, it does not account for the aforementioned ontogeny of paediatric physiology [23], and this remains a key limitation of popPK modelling in paediatric mAb development. Given that the paediatric population is vulnerable to side effects from dosing errors, especially younger infants [24], it is imperative that modelling and simulation approaches account for how physiological differences between paediatrics and adults could affect the pharmacokinetics of mAbs and correspondingly drug exposure levels.
Table 1. Comparison of modelling approaches for mAb pharmacokinetic predictions in the paediatric population.
Methods Allometric Scaling Pop-PK PBPK
Characteristics Empirically derived function, predicting individual PK parameters (e.g., CL and V) based on demographic information (e.g., BW)
(e.g., k = 0.75 for CL, 1.0 for V) [14]		 Prediction based on retrospective analysis of pooled clinical data with the allometric and maturation function incorporated. Can predict within dose range studied or other doses and age range [20]. Based on understanding complex physiological processes to mechanistically predict PK based on the interplay between drug-specific characteristics [25].
Main applications Extrapolate clinical PK information from adults to pediatric patients, typically combined with pop-PK to support the design of pediatric clinical studies [20] Statistically driven analysis of pooled PK data from different clinical studies. Covariate analysis (age, gender, weight) can explain sources of PK variability. [20] Leverage mechanistic mathematical models that recapitulate the physiology of humans, from neonates to adults, to assess the impact of ontogeny on mAb PK [26][27]
Strengths Simple and quick with minimal resources required [14] Analyse sparse data (typical for pediatric studies), and identify covariates that affect PK.
Can integrate complex customised allometric and maturation functions [28].		 Can be used for predictions with limited clinical data. Accounts for the ontogeny of physiological processes in pediatrics, especially younger infants, and its impact on mAb PK [29][30].
Gaps Only captures body-size related information. Limited representation of complex physiological process such as TMDD or FcRn recycling. [20]
Promising for mAbs with linear PK, which is affected by few well-understood parameters. However, less scientifically vigorous compared to pop-PK and PBPK [12].		 Knowledge about the appropriate allometric and maturation functions required.
Predictions limited to scaling of selected parameters within the population and doses studied [28].		 Heavily reliant on biological understanding of ontogeny considering the physiological processes in pediatrics for initial model development.
Reliability of data largely hinges on underlying ontogeny data [30][31].
BW, body weight; CL, clearance; FcRn, neonatal Fc receptor; PK, pharmacokinetics; PBPK, physiologically based pharmacokinetic modelling; pop-PK, population pharmacokinetic modelling; TMDD, Target-mediated drug disposition; V, distribution volume.

2. Physiologically Based Pharmacokinetic Modelling—An Emerging Alternative?

To address the limitations of popPK modelling, PBPK modelling, which is capable of accounting for the ontogeny of key physiological processes, was considered to determine the first-in-paediatric dose for clinical trials. PBPK models leverage differential equations which describe compartments by representing specific tissues linked by blood flow to recapitulate the anatomy and physiology of humans, from neonates to adults [26][27]. Subsequently, ontogeny in paediatric physiology, such as blood flow, lymph flow and biochemical processes, can be incorporated to evaluate their effects on drug exposure [15][32].
The utility of PBPK modelling studies has been established in small molecule drugs and plays a critical role in regulatory submissions to the FDA and EMA [25][29][33][34]. However, it cannot be assumed that similar success can be achieved when PBPK modelling is applied to mAbs, given that the PK of small-molecule and large-molecule drugs are inherently different. Hence, the suitability of PBPK modelling for paediatric mAb development must be evaluated separately.

3. Ontogeny of Key Physiological Processes in Pediatric Monoclonal Antibody Disposition for Exploration in PBPK Studies

To provide perspectives on how PBPK studies could explore the age-dependency of key physiological processes in paediatric mAb disposition, the researchers consolidated physiological data across different age groups from birth to adults and highlighted a few significant findings from a recent review evaluating the current understanding in this area [9][35].
Given that mAbs have a large molecular size, contributing to poor membrane permeability, their distribution is primarily restricted to the plasma and extracellular fluid [36]. Thus, plasma and extracellular fluid volume could be used to estimate the volume and distribution of mAbs. Clinical studies have observed extracellular fluid decreased rapidly from birth, especially over the first few months. Coupled with a modest increase in plasma volume from birth, the net total body volume of distribution was higher in young infants compared to adults [9].
While the extravasation of mAbs in paediatrics has yet to be quantified, the extravasation rates of other plasma proteins, such as albumin, which demonstrate similar distribution patterns and FcRn affinity, could serve as a proxy to explore the ontogeny of mAbs extravasation [37]. Studies have reported the higher extravasation of plasma proteins in neonates compared to adults [9], suggesting a similar trend in mAbs. The possible mechanisms explaining the higher extravasation rate of plasma proteins could be the higher proportion of ‘leaky’ tissues (tissues where capillary permeability is higher) and higher capillary density in young infants compared to adults.
Paediatric pharmacokinetic studies [38][39][40] have also reported a higher absorption rate of therapeutic proteins in infants compared to adults. Since one major absorption pathway of mAbs is lymphatic drainage and recirculation, lymph flow rate provides an indication of mAbs absorption rate [41]. While lymph flow rates have not been quantified in human infants, they are commonly estimated to be 0.2% of the plasma flow [42], which is higher in infants compared to adults.
A major elimination pathway of mAbs is the lysosomal degradation after endocytosis, and clinical evidence of the Fc neonatal receptor (FcRn) role in protecting mAbs from lysosomal degradation has emerged [42]. Approximately 66% of FcRn-bounded mAbs in the vascular endothelium were recycled back to the plasma, prolonging their half-lives [43][44][45][46]. FcRn expression has not been quantified in human infants until very recently when Barber et al. investigated the ontogeny of FcRn expression in paediatric human tissues [47]. The study covered a wide developmental age range, quantifying FcRn expression in paediatric tissues (liver, intestine, kidney and skin). They discovered for the first time a declining trend in FcRn abundance from neonatal to adult levels, confirming an earlier prediction previously made by a minimal PBPK model of IgG [48].
This newfound discovery of a higher FcRn expression in paediatrics compared to adults disputed a prior hypothesis put forth by Malik et al. which proposed that lower FcRn expression levels in paediatrics could be responsible for the increasing mAb clearance observed in young infants [9]. However, there is a caveat to this hypothesis. An elevated mAb clearance in young infants could also be due to their higher level of endogenous IgG concentration after birth, resulting in increased competition for FcRn binding between IgG and mAb. Consequently, the decrease in mAb and FcRn binding could explain the decrease in FcRn recycling of mAb and, thus, increased mAb clearance. At present, an increased mAb clearance cannot be attributed to a single variable but rather can be more accurately explained by a multitude of variables. Thus, exploring the age-dependency of different physiological processes responsible for mAb elimination could provide further insights into this area.

4. Perspectives on Existing Pediatric PBPK Models for Monoclonal Antibodies

An analysis of paediatric PBPK modelling studies for monoclonal antibodies was performed to evaluate the prediction accuracy of existing PBPK models. However, paediatric PBPK studies for mAbs are still limited [1][31][48], despite the increasing appreciation of its utility in accounting for how the ontogeny of paediatric physiology could affect mAb PK [49][50]. At present, there are only seven paediatric PBPK models published [30], and these paediatric PBPK studies are limited to less than 10 mAbs relative to the 39 mAbs currently approved for paediatric usage. This is possible because the dearth of knowledge regarding age-related changes in mAb PK and the ontogeny of paediatric physiology data has hindered PBPK model development which requires rich physiological knowledge [1][31][51].
Infliximab is one of the more well-studied monoclonal antibodies in terms of PBPK modelling. Pan et al. constructed an Infliximab PBPK model for full-term neonates to adolescents [31]. Using this PBPK study as an example, researchers evaluated how PBPK prediction accuracy varied across different paediatric age groups. The area under the curve (AUC) predicted; the over-observed ratio was used as a measure of prediction accuracy. In this study, PBPK modelling across all paediatric age groups had a prediction accuracy that fell within 0.5–1.5-fold change (Figure 1), suggesting that it is reasonably accurate. In the same study, allometric scaling using popPK parameters also found that predicted mean clearance values for infliximab fell within two-fold of the observed data [31]. These findings were also supported by Malik et al., who reported that PBPK models for infliximab achieved predictions within two-fold of the observed concentrations 66.7% of the time for children above 4 years old [5][12]. Additionally, when multiple adult popPK models were allometrically scaled for Infliximab, the poorest model still fell below the two-fold error threshold [12]. Taken together, the PBPK model for Infliximab showed comparable prediction accuracy to the classical approach of applying allometric scaling to popPK parameters.
Pharmaceutics 15 01552 g001 550
Figure 1. Evaluation of prediction accuracy of pediatric PBPK studies for Infliximab. Prediction accuracy of PBPK models across all pediatric age group fall within 0.5–1.5 fold change. h denotes time (in hours). Yellow data point is behind red data point as their values overlap.

This entry is adapted from the peer-reviewed paper 10.3390/pharmaceutics15051552

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