Transcapillary Transport: One-Pore Versus Two-Pore

Although small molecules can enter tissues readily via processes like transcellular diffusion and transporter-mediated uptake, extravasation of TPs is mostly restricted to diffusion and convection through paracellular pores of vascular endothelial wall (Fig. 1B). This process can be mathematically formulated as (Bresler and Groome, 1981):

Where is the organ lymph flow, and are vascular and interstitial concentrations of a tissue, and is the permeability-surface area product. is the tissue vascular reflection coefficient, which represents the fraction of a solute that is unable to cross the pores. Typically, is greater (i.e., higher resistance) for larger-size proteins (Covell et al., 1986) and tissues with less leaky vasculature (Shah and Betts, 2012). For TPs as large as mAbs (150 kDa), the transcapillary transport is considered almost entirely convective (Baxter et al., 1995), and thus the flux from vascular space to interstitial space can be reduced to . Similarly, the mass transport from interstitial fluid to lymphatics is described as , where is the lymphatic reflection coefficient. One crucial assumption underlying these equations is that the pores have a uniform size (homoporosity, or one-pore model), which contradicts the actual physiology.

The two-pore phenomenon was first noted in the 1950s (Grotte, 1956) and further elaborated and mathematically formulated by Rippe and Haraldsson (1987, 1994). Continuous and fenestrated endothelium is assumed to contain two categories of pores, small pores (radius = 4.44 nm) and large pores (radius = 22.85 nm), and protein solutes extravasate by diffusion or convection via both types of pores (Fig. 1B). What makes the two-pore framework distinct from the one-pore model is the incorporation of isogravimetric flow ( ), a circulation flow entering large pores and existing small pores, which can explain a transvascular flux of proteins even in the absence of net filtration (“isogravimetric” state). Accordingly, the actual lymph flows through these pores corrected for are:

Where and are organ lymph flows through large and small pores, and are relative abundance of both pores, and is the net organ lymph flow. Thus, both diffusive and convective processes for protein extravasation can be characterized in the clearance terms as below: and are transcapillary clearances through large and small pores in an organ, and are the permeability-surface area products, and and are vascular reflection coefficients. Particularly, and are the Peclet numbers that reflect the importance of convection versus diffusion in the extravasation process and defined as:

The two-pore formalism has been applied in PBPK models developed for antibodies and antibody fragments since 1994 (Baxter et al., 1994). Early work typically estimated values of certain key parameters, such as , , , and σ (Baxter et al., 1994; Ferl et al., 2005; Davda et al., 2008) as they are difficult to measure and can vary depending on drugs and organs. Unfortunately, these parameter estimates differ substantially between studies and are often associated with high uncertainty and poor identifiability, restricting extensive utilization of the two-pore model. Sepp et al. mathematically derived and and found that they can be expressed as explicit linear functions of . Additionally, they also reported that could be calculated using an empirical equation for (Sepp et al., 2015). Due to these derivations, their model greatly reduced the number of parameters fitted (only needed to be estimated for the two-pore extravasation) and could capture the whole-body PK of a nonbinding domain antibody. In pursuit of development of a generalized PBPK model to characterize a diverse array of TPs, Li and Shah (2019) further expressed glomerular sieving coefficient (discussed later), , , and σ as functions of MW so that the two-pore PBPK model could a priori predict the PK of eight TPs of different sizes without fitting any parameter and facilitate a better understanding of the contribution of diffusion and convection toward protein transport across a wide range of MW.

Special Tissues

In whole-body PBPK models, various tissues are usually treated as modules with similar kinetic processes. However, certain tissues differ significantly in terms of anatomy and physiology (e.g., solid tumor, brain, eye) and may require dedicated PK models for characterization of TP concentrations.

In solid tumors, deficient lymphatic drainage causes high interstitial hydrostatic pressure, which precludes convection of macromolecules. Meanwhile, the capillary wall tends to be more permeable than blood vessels in healthy tissues (Boucher et al., 1990). These features imply that extravasation of TPs is primarily dependent on diffusion. In addition, the solid tumor space can be viewed as a collection of Krogh cylinders, and TP distribution into the tumor can be described as:

(Thurber et al., 2008; Schmidt and Wittrup, 2009; Thurber and Wittrup, 2012). The first term refers to protein permeation through the vascular capillary, and the second term indicates diffusion-driven uptake from the tumor surface that is in contact with the surrounding tissue (surface uptake), as shown in Fig. 1C. , , and represent TP concentrations in the tumor extracellular space, the plasma, and the surrounding tissue. P is the permeability coefficient across the capillary wall, D is the diffusion coefficient, ε is fraction of tumor that is accessible to macromolecules, is the capillary radius, is half the average distance between blood vessels within the tumor, and is tumor radius. It is apparent in the equation that the surface uptake pathway dominates when the tumor is small and less vascularized (i.e., small ), and the TP distribution occurs almost exclusively from extravasation when the tumor is larger and vasculature is well developed. Schmidt and Wittrup also derived empirical relationships between molecular size and , D, and ε by fitting a two-pore model of the capillary wall and a two-pore model of tumor interstitium using experimental data (Schmidt and Wittrup, 2009). These relationships not only help readily calculate tumor uptake for various TPs but also provide insights into the antigen binding affinity required for prolonged retention in the tumor for different-size TPs.

In addition to extravasation into the tumor, heterogeneous intratumoral distribution is of great interest. The heterogeneity is often featured by perivascular distribution of TPs, leading to regions of antigen-positive cells completely untargeted. This phenomenon is attributed to the “binding site barrier,” which refers to high-affinity binding to the target (and subsequent degradation) right after extravasation of the TP preventing deeper distribution into the tumor (Fujimori et al., 1990). A few model structures have been proposed to delineate heterogeneous drug distribution within a tumor, with different levels of complexities. The Krogh cylinder model in conjunction with a PBPK framework has been capable of predicting antigen-bound fraction as a function of the distance from the vasculature and evaluating strategies to overcome the binding site barrier (Cilliers et al., 2016). Shah et al. proposed a spherical tumor model that contains five circular layers with equal thickness, with adjacent layers connected by permeability surface area coefficients (Shah et al., 2009). This model can be modified to characterize the binding site barrier by incorporating target expression and binding in each tumor layer (Glassman and Balthasar, 2019). The simplest approach is to allow only a fraction of tumor antigens to be accessible for binding (Urva et al., 2010), but the associated caveats are complete loss of information about the heterogeneity and poor generalizability of the fraction term when drug doses, binding affinities, or tumor sizes change.

The brain represents another challenging tissue to model as it is known to have a tight blood-brain barrier (BBB) and blood-cerebrospinal fluid barrier (BCSFB), and complex fluid flows in the central nervous system. A generic two-pore PBPK model in PK-Sim platform could fit antibody disposition data in mouse brain reasonably well, and the brain lymph flow and hydraulic conductivity were estimated to be extremely low (Niederalt et al., 2018). Adding a cerebrospinal fluid (CSF) flow exiting the brain that removes macromolecules can further reduce predicted brain exposures of TPs (Brinker et al., 2014; Sepp et al., 2019). Change et al. presented a higher spatial-resolution brain PBPK model that details paracellular transport and transcytosis through BCSFB and BBB, CSF compartmentalization (including lateral ventricle, third-fourth ventricle, cisterna magna, and subarachnoid space), and circulations of CSF and brain extracellular flow (Fig. 1D). This translational model was calibrated using rat disposition data in various brain regions and accurately predicted brain PK of mAbs in mice, monkeys, and humans (Chang et al., 2019).

As the research on eye diseases expands, ocular disposition is of more interest. The eye is characterized by delicate compartmentation, a variety of barriers, and complex fluid dynamics (Perez et al., 2013). Intravitreal dosing is the mainstream administration route in treating ocular disorders such as age-related macular degeneration and diabetic retinopathy, which requires frequent injections and causes pain. On the other hand, several anticancer mAbs and ADCs targeting diverse antigens have been shown to induce ocular toxicities following intravenous injections. As such, modeling drug exposures in the eye is valuable not only for prioritizing suitable TPs during drug development and optimization of dosing regimen but also for evaluating ocular toxicity potential. A novel anatomic ocular compartment that contains cornea, iris-ciliary body, aqueous humor, lens, vitreous humor, retina, choroid, and sclera, embedded in a PBPK framework, has been proposed to characterize ocular disposition following intravitreal or systemic administration (Bussing and Shah, 2020), as shown in Fig. 1E. Considering that current antivascular endothelial growth factor TPs for ocular diseases include IgG, Fc-fusion, antigen-binding fragment (Fab), and single-chain variable fragment formats, the two-pore formalism is anticipated to be incorporated in the ocular compartment to better characterize the ocular absorption and disposition processes.

Interaction with FcRn

FcRn is known to be expressed in vascular endothelial cells of many tissues and hematopoietic cells and contribute to the long serum persistence of IgGs and albumin. Binding between IgGs and FcRn is pH-dependent, with high affinity at acidic pH and negligible binding at physiologic pH. Following fluid phase pinocytosis, IgG binds to FcRn upon acidification of endosomes. Unbound IgG is subject to lysosomal degradation, whereas FcRn-bound IgG is returned to apical or basolateral cell surface. Once exposed to the physiologic pH of extracellular fluid, IgG is released into the systemic circulation or the interstitial fluid.

The PBPK model by Ferl et al. was one of the first to account for FcRn expression in skin and muscle and incorporated IgG uptake into endothelia, IgG binding to FcRn, recycling of FcRn-bound IgG, and degradation of unbound IgG in the endosome (Ferl et al., 2005). The PBPK model proposed by Garg and Balthasar accounted for FcRn expression in all the tissues and characterized mAb PK in wild-type and FcRn-knockout mice (Garg and Balthasar, 2007). As shown in Fig. 1F, they assumed an equilibrium binding between mAb and FcRn in the endosomal space. Therefore, within the endosomal compartment, unbound IgG was expressed as a fraction ( ) of total IgG, and was defined as:

Above, is the total endosomal IgG concentration in an organ, is the total FcRn concentration, and is the dissociation constant of the antibody for binding to FcRn. Compared with the model by Ferl, in this model the organ-specific FcRn capacity was assumed to be proportional to organ weight and adjusted based on total FcRn amount in the body. This model estimated the fraction of FcRn-bound antibody that recycles back to the vascular space ( ) to be 0.715, and this value has been carried forward in many subsequent PBPK models. Nowadays, FcRn-mediated recycling is routinely incorporated within mAb PBPK models, with variable assumptions across models.

Prompted by the fact that the equilibrium binding model over-predicts the improvement in antibody half-life with increasing equilibrium affinity of FcRn binding at pH 6, Chen and Balthasar (2012) constructed a catenary FcRn binding model that describes endosomal transit process, gradual acidification of endosomes, and kinetic binding between the antibody and FcRn. This was justified by the fact that residence time in endosomes (∼10.8 minute) is too short for the mAb-FcRn interaction to reach equilibrium (dissociation half-life of 6–58 minute). In this catenary model, the vascular endosomal space is subdivided into five transit compartments with pH of 7.4, 7.0, 6.5, 6.0, and 6.0, and the transit time ( τ) was determined to be 2.7 minute through each compartment (Fig. 1F). FcRn was allowed to interact with IgG using pH-dependent association rates ( ) and dissociation rates ( ) in each subcompartment, and only in the last subcompartment unbound IgG was assumed to be cleared and bound IgG was assumed to recycle. Of note, an underlying constraint between the tissue endosomal subcompartment volume ( ) and uptake clearance ( ) exists in this model to ensure endosomal volume homeostasis and FcRn and endogenous IgG steady-state:

Additionally, this model employs measured tissue-specific FcRn expression (Li and Balthasar, 2018), contrary to assuming a universal FcRn concentration across tissues in prior models (Urva et al., 2010; Shah and Betts, 2012). The equilibrium binding model performed as well as the catenary model in predicting PK of normal mAbs. However, the former predicts an almost proportional increase in mAb half-life with enhanced FcRn binding, whereas the latter predicts much more modest half-life extension, which is more consistent with observations reported in the literature (Chen and Balthasar, 2012).

In prior models, FcRn concentrations are assumed to be static (either tissue-specific or universal), which makes them inappropriate for characterizing PK of anti-FcRn mAbs used in treating autoimmune diseases as FcRn density is expected to be altered under the treatment. Accordingly, on top of the catenary model, Li and Balthasar (2019) took account of FcRn turnover by supplementing an FcRn pool compartment, where synthesis and degradation of FcRn can occur. This pool compartment provides the first endosomal subcompartment with FcRn and collects unbound and IgG-Fc–bound FcRn from the last endosomal subcompartment at a rate of . Anti-FcRn mAbs are allowed to bind to FcRn through either the Fab arms or Fc region. In the last subcompartment, Fc-bound mAb is recycled, whereas mAb-FcRn complexes linked via the Fab domain are either degraded or recycled directly to the first subcompartment. Such model scheme allows characterization of FcRn depletion in the presence of FcRn inhibitors.

There is also a growing interest in the role of hematopoietic cells in mAb PK (Challa et al., 2019; Malik and Edginton, 2020), as FcRn is expressed in these cells (Vidarsson et al., 2006; Latvala et al., 2017), and hematopoietic cells exist ubiquitously in the body (Yu et al., 2016). Experiments involving wild-type and hematopoietic cell-specific FcRn knockout mice have shown that endothelial and hematopoietic cells almost contributed equally to FcRn-mediated salvage (Akilesh et al., 2007; Montoyo et al., 2009; Richter et al., 2018). Correspondingly, the model mentioned above also considers mAb-FcRn interactions in endosomal space associated with hematopoietic cells, which are assumed to reside within the lymph, tissue interstitial space, and plasma (Li and Balthasar, 2019). However, widespread validation of such model remains unaccomplished.

Target-Mediated Drug Disposition

Target-mediated drug disposition (TMDD), a term coined by Levy (1994), refers to situations where high-affinity binding to the target impacts the PK characteristics of the drug. TMDD applies to the majority of TPs as this class of drugs is designed to have high affinity and specificity for their targets. However, its impact on the PK is only noticeable at lower TP concentrations when this pathway is not saturated. TMDD modeling has a history of about 20 years, and many seminal works have been done to establish the mechanistic framework, evaluate the identifiability of model parameters, simplify the model equations, and modify the model to characterize novel molecules. Interested readers are referred to (Mager and Krzyzanski, 2005; Gibiansky et al., 2008; Marathe et al., 2009; Gibiansky and Gibiansky, 2014; Lavezzi et al., 2018; Peletier and Gabrielsson, 2018) for detailed education on this topic. Only a few TMDD models will be highlighted below.

A full, generic TMDD model was proposed by Mager and Jusko (2001). The model assumes that drug in the central compartment ( ) can distribute into the peripheral compartment ( ), be cleared in its free form (via the elimination rate ), or bind (rate constant, ) to the free target ( R) to form the drug-receptor complex ( ). R is produced (via the synthesis rate, ) and eliminated (via the degradation rate, ) in the central compartment, whereas may undergo dissociation ( ) or internalization ( ). For soluble targets, is usually close to of the free drug. For membrane-bound targets, is often similar to of the free receptor, and when this holds, the total receptor concentration ( ) is constant, which eliminates the need to mathematically define temporal changes in . Several approximations of the full TMDD model can be implemented to avoid estimating practically unidentifiable parameters such as and , and to make the equations less stiff. For example, when , , and are much faster than the remaining processes, it can be assumed that the free drug, the free receptor, and complex are at quasi-steady-state, with at all times, where represents (Gibiansky et al., 2008). If is much smaller than , the assumption of quasi-equilibrium becomes valid, where , with being (Mager and Krzyzanski, 2005). Furthermore, if , both quasi-steady-state and quasi-equilibrium models can be reduced to Michaelis-Menten approximation (Gibiansky et al., 2008).

PBPK models usually adopt the full, quasi-steady-state, or quasi-equilibrium TMDD component in the tumor compartment (Urva et al., 2010; Shah and Betts, 2012; Cao and Jusko, 2014), using receptor and drug concentrations at the site of action instead of the central compartment (Fig. 1G). Parameters such as , , , , or even to operate these models could be obtained from in vitro measurements. Other physiologically relevant issues also need to be considered by these models, such as tumor antigen accessibility (discussed in Distribution Models) and antigen shedding. Many membrane-bound targets are known to shed the ectodomains or have a soluble form in the body (Kuang et al., 2010). This circulating pool competes with the cell-surface counterpart for drug binding and alters disposition and efficacy of the TP. A mPBPK-TMDD model incorporating the target-shedding mechanism of human epidermal growth factor receptor 2 (HER2) has systematically evaluated the effects of target shedding kinetics, soluble target turnover, and affinity difference between cell-surface HER2 and shed HER2, on plasma PK of trastuzumab (an anti-HER2 antibody), as well as receptor occupancy of membrane-bound HER2 (Li et al., 2014). Such TMDD model with target shedding was further modified and incorporated into a population PBPK framework to demonstrate the concentration of shed HER2 ectodomain as one of the key factors that contribute toward interindividual variability in trastuzumab PK (Malik et al., 2017).

Renal Elimination

Kidney contributes to both catabolism and excretion of TPs. Renal excretion is a highly size-dependent process, and therefore is an important determinant of PK differences observed for TPs across a wide MW range. The molecular size cutoff for glomerular filtration is approximately 60 kD, and an inverse sigmoidal relationship between glomerular sieving coefficient (GSC) and MW has been demonstrated (Haraldsson et al., 2008). The size selectivity makes the renal filtration process fit well with the two-pore model, where GSCs can be expressed as empirical functions of MW (Li and Shah, 2019; Sepp et al., 2019). A pathway analysis reveals that lysosomal catabolism dominates the elimination of TPs with MW greater than 100 kD, and TPs less than 100 kDa are mostly eliminated via renal filtration (Li and Shah, 2019). In addition to size, other TP-specific factors also play a role in glomerular filtration. A positive correlation between GSC and molecular charge was also observed, which is attributed to the negatively charged glycocalyx of the filtration barrier (Haraldsson et al., 2008). A recent study reveals that renal filtration is higher for one-armed (nonbranched) IgGs and IgGs with a more flexible hinge region (Rafidi et al., 2021). More quantitative data on systemic and kidney PK of TPs are needed to incorporate factors such as charge, shape, and rigidity as covariates in PK models. Similar to other organs, renal endothelial cells can take up TPs for lysosomal degradation. Additionally, renal catabolism can occur via reabsorption into endocytic vesicles of proximal tubule cells following glomerular filtration or by peritubular extraction of TPs from postglomerular capillaries (Meibohm and Zhou, 2012), as shown in Fig. 1H. However, these catabolism mechanisms have not been explicitly accounted for in most current models.

Immunogenicity

TPs with antigenic components, such as foreign sequences and impurities, can induce immunogenicity and ADA formation. Although in rare cases ADAs serve as a storage depot that sustains TP exposures (Wang et al., 2008), they often compromise drug PK to varying degrees, depending on ADA titer, kinetics, and types (neutralizing or non-neutralizing).

Empirical models with time-varying PK parameters have been used to study the impact of immunogenicity on PK of TPs. For example, a parameter termed change in clearance time ( α) was included in a two-compartment TMDD model, which allows the linear clearance to suddenly change at time α, presumably due to ADA formation (Kathman et al., 2016). It was found that the derived can be much earlier than the time when ADA is detected in bioassays and thus is helpful in identifying false-negative subjects. Certain more mechanistic models introduce a compartment representing ADA depot that is activated by repeated dosing of TPs, and after some transit time, ADAs are available in the central compartment to bind to TPs and get eliminated (Chen et al., 2013; Perez Ruixo et al., 2013). The dissociation rate of ADAs binding to TPs can decay over time to reflect affinity maturation (Chen et al., 2013).

These models are useful to determine the onset and magnitude of influence of ADA on TP disposition but cannot project immunogenicity based on in vitro and preclinical data. To aid the immunogenicity risk assessment during drug development, Chen et al. (2014a, 2014b) proposed a mechanistic, multiscale, prototype framework that integrates subcellular, cellular, and whole-body level models. The subcellular model focuses on antigen presentation processes, including activation of dendritic cells by endotoxin existent in TP formulations, internalization of the antigenic TP, degradation of the TP into T-cell epitope peptides, binding between peptides and major histocompatibility complex class II molecules, and presentation of T-epitope-MHC complexes on dendritic cell membrane. The cellular model further recapitulates activation, proliferation, and differentiation of T helper cells and B cells, which ultimately results in the secretion of ADAs from plasma cells. Total 17 subgroups of B cells and derived ADAs are assumed with incremental binding affinities to the antigenic TP so that the affinity maturation can be captured. The differential equation–based subcellular and cellular models are readily integrated with an extended two-compartment model to describe in vivo disposition of ADAs and the TP. Modeling the impact of immunogenicity on PK is achieved by adding the following two components to the clearance of TP: (1) rapid elimination of ADA-TP immune complex and (2) internalization of TP following B-cell receptor binding. Of note, such mechanistic modeling allows for incorporating drug-specific, product-specific, and patient-specific characteristics and can generate predictions of immunogenicity incidence and its influence on PK of TPs in a given population.

Interantibody Differences in PK

The most prominent feature that distinguishes diverse TPs is the MW range (3.5 kDa to 150 kDa) and the size-dependent ADME mechanisms have been decently characterized using two-pore models. Nonetheless, mAbs with MW of around 150 kDa can still display very distinct PK profiles, even in cases where FcRn affinities are similar and TMDD is absent. Other key factors that are known to govern ADME properties of mAbs include charge, glycosylation, and shape, and some emerging models have attempted to address their influences on mAb PK.

Chen and Balthasar (2012) suggested two coefficients, and , that can be applied to modify the endosomal uptake clearance ( ) and transcapillary convection ( ), respectively, to account for interantibody variability in PK (Glassman et al., 2015). Using a similar yet augmented approach, Hu and D’Argenio (2020) applied four mAb-specific parameters in a PBPK model that represent antibody variabilities in the pinocytosis rate ( ), diffusive/convective transport rates ( ), and lymphatic capillary uptake rate ( ) and presystemic clearance ( ) following subcutaneous dosing to individually fit intravenous and subcutaneous PK data of many mAbs in the training dataset. It was found that parameter estimates of both and positively correlated with positive charges of the complementarity-determining region vicinity (PPC), consistent with the observations that increased positive charges of mAbs lead to enhanced cellular uptake and lower bioavailability (Bumbaca Yadav et al., 2015; Datta-Mannan et al., 2015). When these two relationships based on PPC were incorporated in the model, they were able to a priori predict subcutaneous PK of 3 out of 4 mAbs in the test dataset. Jones et al. (2019) also modeled the impact of molecular charge on mAb PK, using a somewhat different approach. Contrary to the predictor used in the model by Hu and D’Argenio (2020), PPC, which is theoretically calculated, this work used the data from affinity-capture self-interaction nanoparticle spectroscopy, an in vitro assay that detects nonspecific charge-based interactions as a covariate in the PBPK model. Instead of moderating the pinocytosis rate, the authors involved nonspecific binding between mAbs and cell membrane sites and internalization of the membrane-bound complex via pinocytosis. The nonspecific binding equilibrium constant was expressed as a function of scores from the in vitro assay, whereby greater self-association leading to higher binding affinity. After model calibration, mAb PK in mice and humans was predicted relatively well.

Modeling approaches incorporating the impact of glycosylation on mAb PK are less explored. This is in part due to inconsistent observations of the effect of high mannose (Kanda et al., 2007; Yu et al., 2012), hybrid type glycans (Kanda et al., 2007; Falck et al., 2021), and galactosylation (Wright et al., 2000; Falck et al., 2021) on clearance. As glycoengineering of TPs gain more popularity, it can be expected that glycosylation will be an important covariate in PK models. As has been discussed in Renal Elimination, more data are needed to substantiate the relationship between molecular shape, charge, or rigidity and kidney elimination. Ultimately, a platform PBPK model incorporating physicochemical characteristics of TPs holds promise for predicting PK based on in silico and in vitro properties and guiding protein engineering strategies to achieve maximum and specific exposure at the site of action.

Modeling Next-Generation TPs

Innovative protein engineering technologies have paved the way for the generation of an array of novel TPs, which often possess unique physicochemical properties and mechanisms of action. Mechanistic models need to be evolved to understand determinants controlling their PK/PD and to aid in rational design of these next-generation modalities. Previous models may work well in capturing existent data, but novel molecules and unusual scenarios often require reexamination of model assumptions and refinement of model structures. Based on the types of modeling efforts required to elucidate the ADME mechanisms, these next-generation TPs can be roughly categorized into two nonexclusive groups.

The first group of TPs necessitate highly mechanistic cellular models to characterize intracellular processing, due to endosomal-environment switch mechanisms, or their intracellular-delivery nature. The well-known concept of “recycling antibodies” has been applied to IL-6R, IL-6, and PCSK 9, where antibodies bind tightly to the target extracellularly at pH 7.4 or with low calcium ion concentration and dissociate rapidly from the target in endosomes where pH is low or calcium ion is concentrated. These “catch-and-release” mAbs are expected to improve neutralization of soluble targets, and detailed modeling work has identified target turnover and pH-dependent binding affinity difference that maximize target clearance (Glassman and Balthasar, 2016a; Yang et al., 2017; Jones et al., 2020). The acid-switched binding is also of interest for membrane-bound targets (Kang et al., 2019), but relevant modeling analysis is scarce. ADCs are a class of drugs for which complex ADME properties have been extensively evaluated in PKPD models. Multiscale models have been developed to characterize systemic disposition and intracellular processing of the intact ADC, the antibody moiety, and the free drug (Maass et al., 2016; Khot et al., 2017; Singh and Shah, 2017a; Singh and Shah, 2017b; Singh et al., 2020). In recent years, biparatopic ADCs that recognize two nonoverlapping epitopes of the same target have gained much interest. Such constructs are believed to promote receptor clustering, inhibit receptor recycling, and promote lysosomal trafficking of the receptor and ADCs, thereby increasing cytotoxic drug release, as exemplified by biparatopic ADCs targeting HER2 (Li et al., 2016), MET (DaSilva et al., 2021), and EGFR (Fan et al., 2021). A mechanistic model can provide a quantitative understanding of the extent to which intracellular trafficking is altered, the resultant drug exposure improvement, and the long-term impact of receptor downregulation caused by clustering.

The second type of next-generation TPs use previously less-understood receptor-mediated disposition pathways. For example, exploiting transferrin receptor (TfR)-mediated transcytosis has been recognized as a strategy to enhance drug delivery across the BBB, as demonstrated using ADCs or bispecific mAbs targeting both TfR and therapeutic targets (Friden et al., 1991). Intriguingly, higher antibody exposure in the brain is observed when TfR engagement is monovalent or has only intermediate affinity (Yu et al., 2011; Niewoehner et al., 2014; Chang et al., 2021b), which ensures both sufficient TfR binding at the luminal side and efficient release to the abluminal side. Empirical and semimechanistic PKPD models have been explored to predict the optimal TfR affinity that maximizes brain exposure, as well as the impact on efficacy and safety (Gadkar et al., 2016; Kanodia et al., 2016). However, the influence of TfR binding valency has not been characterized, and translation of mouse and monkey models to human remains to be validated. Fc gamma receptor (FcγR)-mediated elimination represents another pathway that can play a role in TP disposition. FcγRs are known to be important in immune responses to mAbs, but their role in mAb PK is less defined. Although experiments with FcγR knockout mice suggested a minor role of FcγR in PK of a typical IgG1 molecule (Abuqayyas and Balthasar, 2012), for those TPs that can form large, high-order immune complexes with soluble ligands (Mortensen et al., 2012; Kasturirangan et al., 2017), or that have significant ADCC resulting in target cell depletion (Uchida et al., 2004), interaction with FcγRs may impact TP disposition. However, a systematic modeling analysis of this pathway is currently lacking. Another modality that falls in this category is albumin-fusion or albumin binding domain (ABD)-conjugated TPs, which leverages pH-dependent albumin-FcRn interactions to achieve half-life extension. This strategy has been applied to IgA (Meyer et al., 2016), single-chain diabodies (Stork et al., 2007), single-chain variable fragments (Sanches et al., 2020), domain antibodies (Goodall et al., 2015), and affibodies (Andersen et al., 2011). A PBPK framework with the two-pore formalism has been demonstrated to well characterize PK of albumin, ABDs, and ABD-conjugated diabody (Sepp et al., 2019; Sepp et al., 2020). However, a more extensive evaluation of how the size of the unmodified TP and the size of the albumin-binding modality/albumin variants impact the half-life of TPs is still needed.