Nonlinear pharmacokinetics of therapeutic proteins resulting from receptor mediated endocytosis
 1.9k Downloads
 34 Citations
Abstract
Receptor mediated endocytosis (RME) plays a major role in the disposition of therapeutic protein drugs in the body. It is suspected to be a major source of nonlinear pharmacokinetic behavior observed in clinical pharmacokinetic data. So far, mostly empirical or semimechanistic approaches have been used to represent RME. A thorough understanding of the impact of the properties of the drug and of the receptor system on the resulting nonlinear disposition is still missing, as is how to best represent RME in pharmacokinetic models. In this article, we present a detailed mechanistic model of RME that explicitly takes into account receptor binding and trafficking inside the cell and that is used to derive reduced models of RME which retain a mechanistic interpretation. We find that RME can be described by an extended Michaelis–Menten model that accounts for both the distribution and the elimination aspect of RME. If the amount of drug in the receptor system is negligible a standard Michaelis–Menten model is capable of describing the elimination by RME. Notably, a receptor system can efficiently eliminate drug from the extracellular space even if the total number of receptors is small. We find that drug elimination by RME can result in substantial nonlinear pharmacokinetics. The extent of nonlinearity is higher for drug/receptor systems with higher receptor availability at the membrane, or faster internalization and degradation of extracellular drug. Our approach is exemplified for the epidermal growth factor receptor system.
Keywords
Recepter mediated endocytosis Nonlinear pharmacokinetics Michaelis–Menten Therapeutic proteins Biopharmaceuticals Epidermal growth factor receptor Nonlinear dispostition Receptor trafficking AntibodiesIntroduction
In recent years, therapeutic proteins have been a major focus of research and development activities in the pharmaceutical industry [1]. Currently, approximately 100 therapeutic proteins have been approved for human use, most of them being biotechnologyderived drug products and many more are under development. Important classes of therapeutic proteins are monoclonal antibodies, growth factors, and cytokines. Generally, therapeutic proteins provide highly attractive but sometimes exceptional behavior in the body [2]: their significant therapeutic potential results from their ability to bind—with high affinity—to specific targets such as receptors or cell surface proteins. For many protein drugs receptor mediated endocytosis (RME) is an important route of cellular uptake and disposition [3]. RME is the process of binding of an endogenous or exogenous ligand to a receptor and subsequent internalization of the resulting complex forming an endosome. Within the cell, the complex may be recycled to the cell surface or intracellularly be cleaved [4, 5]. Receptormediated uptake plays a major role in the elimination of protein drugs from the body [3] and is suspected to be a major source for the nonlinear pharmacokinetic (PK) behavior that is observed in clinical data for numerous protein drugs [6].
When aiming at analyzing preclinical/clinical pharmacokinetic data of protein drug trials, typically empirical 1, 2 or 3compartmental models including linear and/or nonlinear disposition processes have been developed. Michaelis–Menten terms have often been used to analyze experimental data in order to account for the observed nonlinearity [7, 8, 9, 10, 11]. These models have been selected based on, e.g., established statistical criteria (such as maximum likelihood), the precision of estimates of model parameters, and in few cases on model evaluation techniques [12, 13, 14, 15]. However, being empirical in nature, these models do not provide a mechanistic understanding of how the different processes of receptor trafficking contribute to the overall pharmacokinetic profile, which is expected to guide, e.g., lead optimization or the design of more efficient dosing regimens. Equally important, there is no theoretical background as to when use the different existing empirical models for nonlinearity.
Less often, models have been developed that also include mechanistic terms to account for nonlinear phenomena, most prominently in terms of targetmediated drug disposition (TMDD) models [16, 17, 18]. TMDD explicitly accounts for binding to a target and potential degradation of the resulting complex. Although originally developed to describe effects of extensive drug target binding in tissues, TMDD has more recently gained interest as a model for saturable elimination mechanisms for specific peptide and protein drugs, including RME [6, 18, 19]. TMDD is a general approach for situations where the interaction of a drug with its target is considered to be relevant and might affect the concentrationtime profiles. However, it does not explicitly take into account the particular features of receptor trafficking inside cells, such as recycling and sorting, i.e., the process by which receptors and ligands are either targeted for intracellular degradation or recycled to the surface for successive rounds of trafficking [20].
There is a considerable amount of literature about detailed mechanistic descriptions of receptor trafficking systems in the systems biology literature (see, e.g., [5, 21] and references therein). Based on these receptor trafficking systems, our approach is to build a general detailed mechanistic model of RME that takes into account the most relevant kinetic processes of drug binding and receptor trafficking inside the cell. Detailed models derived from the underlying biochemical reaction network have the advantage of a mechanistic interpretation of the kinetic processes and estimated parameters. In [22], a celllevel model of the cytokine granulocyte colonystimulating factor (GCSF) and its receptor was incorporated into a pharmacokinetic/pharmacodynamic model to allow for analyzing the life span and potency of the ligand in vivo. However, often these advantages come along with the disadvantage of containing more parameters which, e.g., in population PK analysis of clinal trials may result in poorer performance in the model selection process, since models containing more parameters are usually penalized by the corresponding model selection criteria.
The objective of this article is to develop a framework for RME that is specifically tailored to the needs in PK analysis of clinical trials by bridging the points of view in pharmacokinetics and systems biology. The aims are (i) to develop a detailed model that takes into account the most relevant processes in relation to receptor trafficking; (ii) to derive reduced models of RME which retain a mechanistic interpretation and are defined in terms of a few parameters only, (iii) to offer guidance as to when use them, and (iv) to analyze the impact of the different processes on the extent of nonlinearity. While our approach applies to many receptor systems in general, we will use the epidermal growth factor receptor (EGFR) signalling pathway to illustrate the approach. The EGFR system has been intensively studied over the past 20 years and is one of the most important pathways for cell growth and proliferation as well as angiogenesis and metastasis [23]. The EGFR system comprises a tyrosine kinase receptor, which is activated by a variety of ligands such as the epidermal growth factor (EGF) or the transforming growth factorα (TGFα) [24, 25, 26]. Mathematical modelling of the EGFR system has proven to be useful for both, measurement of rate constants [27] as well as to elucidate the effects of receptor trafficking as an input to downstream signalling cascades [21, 28]. From a therapeutic point of view, the EGFR system has shown to be a promising target in cancer therapy [29, 30]. Several agents, including therapeutic proteins such as monoclonal antibodies (mAbs), have been developed to specifically target the EGFR with some already approved for drug treatment [31, 32, 33].
Theoretical
Throughout the article, the term ’ligand’ refers to both a physiological ligand as well as an exogenous drug ligand.
Detailed model of RME (Model A)
Reduced models of RME
One objective of this study is to derive and analyze reduced models of RME that capture the impact of receptor dynamics on the distribution and elimination of a ligand and that still allow for a mechanistic interpretation. While during short time intervals the transient redistribution processes between the different receptor species R _{m}, RL _{m}, RL _{i} and R _{i} may be of interest, these are usually assumed to be negligible on time scales of interest in pharmacokinetics. Therefore, our approach to reduce the detailed RME model will be based on the assumption that the receptor species R _{m}, RL _{m}, RL _{i} and R _{i} are in quasisteady state. In order to finally derive reduced models of RME, it is necessary to make an additional assumption on the timescale of receptor synthesis and degradation. We distinguish the following two scenarios: (1) the time scale of receptor synthesis and degradation is slow in comparison to the time scale of ligand disposition. In this case, we formally set k _{synth} = k _{degR} = k _{degRL} = 0. As a consequence, the total number of receptors in the system remains constant. Or, (2) the time scale of receptor synthesis and degradation is fast, i.e., comparable to the redistribution processes of the different receptor species. Both scenarios will be used in the following to establish a link between the reduced and the detailed model.
Reduced model of saturable distribution into the receptor system and linear degradation (Model B)
The idea in deriving a reduced model of RME is to use the quasisteady state assumption for the receptor system (RS). This transforms the differential equations (2)–(5) into algebraic equations for R _{m}, RL _{m}, RL _{i}, R _{i}. For a given number of extracellular ligand molecules L _{ex}, these algebraic equations can be solved explicitly. This allows us to compute the total number of ligand molecules in the receptor system L _{RS} = RL _{m} + RL _{i} as a function of the extracellular number of ligands L _{ex}. Based on L _{RS}, the quasisteady state number of intracellular ligand–receptor complexes RL _{i} can be computed, which determines the extent of elimination.
Reduced model of saturable degradation (Model C)
Integration of RME into compartmental PK models
As in this article we are interested in how to represent RME in PK models, the below mentioned system of ODEs based on the reduced Models B and C represent the proposed structural PK model that can be used for parameter estimation in PK data analysis of nonclinical and clinical trials. The parameter values are determined by performing a fit of the model to the specific in vivo data. Alternatively, the model might be used to scaleup in vitro derived RME parameter values to the in vivo situation (see also Discussion).
Nonlinear PK caused by RME
In this section, we investigate the extent of nonlinearity in the context of the Michaelis–Menten model defined in Eqs. 24 and 25. We aim to examine the effect of drug and cell properties on the nonlinearity of the pharmacokinetics, e.g., different drug affinities to the receptor (different k _{on} and k _{off} values) or different rates of internalization and recycling of the drug in different cells.
Contribution of the different parameters to the extent of nonlinearity
Increase of parameter  Resulting change in extent of nonlinearity 

R _{0}  ↑ (RS) 
k _{recyR}  ↑ (RS) 
k _{on}  ↑ (L) 
k _{lyso}  ↑ (RS & L) 
k _{interRL}  ↑ (RS & L) 
k _{off}  ↓ (L) 
k _{interR}  ↓ (RS) 
k _{recyRL}  ↓ (RS & L) 
Methods
In order to simulate Models A, B and C, we numerically solved the corresponding system of ODE’s with Matlabs builtin ode15s integrator (The Mathworks, Inc., Natick/MA, USA, version 7.4). Parameter values for the reduced Models B and C were derived from those of Model A using the established relations (12)–(14), and (19) and (13), respectively. Subsequently, numbers of molecules where converted into concentrations (nM).
The models were compared based on the simulated extracellular drug concentration. The specific details of the simulation studies are given in the respective Result section to allow for an easier comparison.
EGFR system with endogenous/physiological ligand
Parameter values for the EGF/EGFR system
Parameter  Numerical value 

k _{on}  5.82 1/(nM h) 
k _{off}  14.4 1/h 
R _{m} ^{(SS)}  2 × 10^{5} molecules 
k _{recyR}  3.84 1/h 
k _{interR}  4.2 1/h 
k _{degR}  0.96 1/h 
k _{recyRL}  1.2 1/h 
k _{interRL}  15 1/h 
k _{degRL}  1.2 1/h 
V _{γ}  4 × 10^{−10} 1/cell 
Hendriks et al. [20, 34] explored EGF as ligand to measure rate constants of the EGFR system. Since receptor is degraded as a consequence of ligand degradation, we choose the scenario of fast receptor synthesis and degradation for all investigations, i.e., Eqs. 12–14 and 19. However, not all rate constants of the herein proposed detailed model of RME were explicitly measured in [20, 34]. Since EGF is predominantly degraded from the EGFreceptor complex [5] rather than from the free form, we set k _{break} = 0 resulting in k _{lyso} = k _{degRL} ≠ 0. Since the parameter k _{synth} was not available in literature, we used the steady state assumption for the receptor system prior to any ligand administration and the experimentally measured steady state number of membrane receptor R _{m} ^{(SS)} [28] to determine k _{synth} using the relation k _{synth} = k _{degR} · R _{i} ^{(SS)} with R _{i} ^{(SS)} = R _{m} ^{(SS)} · k _{interR}/k _{recyR}. The initial number of receptors are R _{m}(0) = R _{m} ^{(SS)} , R _{i}(0) = R _{i} ^{(SS)} , and RL _{m}(0) = RL _{i}(0) = 0; the initial concentration of extracellular ligand is L _{ex}(0) = 40 nM.
EGFR system with exogenous/therapeutic protein ligand
Parameter values used by Lammerts van Bueren et al. [11]
Parameter  Numerical value 

V _{pl}  35 ml/kg 
V _{int}  70 ml/kg 
\(\widehat{B}_{\rm max}\)  2 mg/kg 
k _{ip}  0.043 1/h 
k _{pi}  0.043 1/h 
k _{b}  0.069 1/h 
k _{ el }  0.0055 1/h 
\( \widehat{k}_{\rm deg} \)  0.005 1/h 
K _{M}  0.5 μg/ml 
A _{pl}(0)  2 and 20 mg/kg 
h  1.0 
Transforming the system of ODEs (30)–(32) from units [mg/kg] to [mg/ml] by dividing by the corresponding volumes yields equations for C _{pl} = A _{pl}/V _{pl}, C _{int} = A _{ int }/V _{int}, C _{b} = A _{b}/V _{int}, in terms of the following scaled parameters q _{12} = V _{pl} · k _{pi}, q _{21} = V _{int} · k _{ip}, \(\hbox{CL}_{\rm lin}=k_{\rm el}\cdot V_{\rm pl}, B_{\rm max}=\widehat{B}_{\rm max}/V_{\rm int}, \hbox{CL}_{\rm RS}= \widehat{k}_{\rm deg} \cdot V_{\rm int}\). The model (30)–(32) scaled to units [mg/ml] can be directly compared to our PK model (20)–(23) with C _{1} = C _{pl}, C _{ex} = C _{int} and C _{RS} = C _{b}, parameterized with the scaled parameters above. We remark that alternatively, our compartmental PK models could have been stated in units [mg/kg].
Results
RME for the EGF/EGFR system: an example for ligand–receptor interaction
For all subsequent in silico studies, the parameter values are stated as given in section “EGFR system with endogenous/physiological ligand”, unless stated otherwise.
Influence of receptor system properties on RME
In order to study the impact of L _{RS} on the approximation quality of Model C, we artificially decrease k _{degRL} by a factor of 10. All other parameters of the detailed Model A, including the initial EGF concentration, are identical. Parameters of Model B and C have been recalculated according to Eqs. 12–14 and Eqs. 19 and 13, respectively, resulting in particular in an increased maximal binding capacity B _{max}. The predictions of the concentrationtime profile of the extracellular EGF concentration C _{ex} based on the three Models A, B and C are shown in Fig. 4(right). While Models A and B give almost identical results, the prediction based on Model C differs significantly. Model C overpredicts the extent of elimination by RME. As shown in Fig. 5 the overprediction corresponds to periods in time where the assumption (15) is violated: While B _{max}/(K _{ M } + C _{ex}) is small for both settings up to time 60 h, it starts to increase thereafter, in particular for the setting corresponding to Fig. 4(right).
Influence of different cell types on RME
RME in the monoclonal antibody/EGFR system: an example for therapeutic protein–receptor interaction
In this section we will illustrate how our unified theoretical approach to RME allows for resolving seemingly contradictory statements about the performance of empirical models of RME. In [11], Lammerts van Bueren et al. reported about a preclinical study involving a mAb against EGFR in monkeys and their subsequent data analysis. They developed a twocompartment pharmacokinetic model comprising a firstorder elimination of the mAb from plasma, a binding compartment (representing EGFRexpressing cells) that equilibrates with the interstitial compartment, and a saturable internalization and degradation of bound mAb. For a detailed description of the model and the corresponding parameters see section “EGFR system with exogenous/therapeutic protein ligand”. Lammerts van Bueren et al. concluded that the observed nonlinear decrease of mAb concentrations in cynomolgus monkeys could not be explained by a saturable elimination in terms of a Michaelis–Menten model and proposed an alternative model, which described the data well. In a different study, the Michaelis–Menten model was reported to successfully describe in vivo data for a monoclonal antibody [10].
The difference between the predictions based on Model B and C should disappear, if the maximal binding capacity is sufficiently decreased. This is shown in Fig. 7(right), where the binding capacity B _{max} has been decreased to one 20th of its original value.
In summary, the inference made in [11] that a Michaelis–Menten term is not adequate for modeling the nonlinearity present in the data is valid for the specific conditions of their experimental design. However, this cannot be generalized to a statement about the validity of the MichaelMenten approximation of RME, as can be seen from Fig. 7(right) and also from the results presented in section “RME for the EGF/EGFR system”.
Discussion
Drugs that demonstrate nonlinear pharmacokinetic behavior at therapeutic concentrations often cause difficulties in designing dosage regimens and determining relations between drug concentrations and effects. The theoretical bases and potential causes of nonlinear/dosedependent pharmacokinetics are manyfold and have been extensively reviewed (see [17] and reference therein). Therapeutic proteins bind with high affinity to specific targets. For many protein drugs elimination by RME plays a major role in their elimination from the body [3]. RME is suspected to be a major source for the nonlinear pharmacokinetic behavior that is observed in pre/clinical data of numerous protein drugs [6]. In this article we theoretically investigated the process of RME on the pharmacokinetics of therapeutic proteins.
The detailed Model A (see Fig. 1) represents RME for an endogenous compound in terms of a system of biochemical reactions (1)–(5), including the binding of the ligand to the receptor, subsequent internalization of the complex and eventually degradation as well as receptor recycling, degradation and synthesis. Two reduced models have been derived under the assumption that the redistribution processes between the receptor species R _{m}, RL _{m}, R _{i} and RL _{i} are in quasisteadystate. For the EGFR system, this assumption has been shown experimentally [27]. For other receptor systems, the steady state assumption seems reasonable since intracellular processes are typically much faster than the time scale of interest in pharmacokinetic studies.
 1.
Distribution as a consequence of the drug binding to the receptor and subsequent internalization of the complex; and
 2.
Elimination as a consequence of endocytosis.

Model B: Elimination and distribution of ligand into the receptor system are important processes to be considered.

Model C: The distribution of ligand into the receptor system can be neglected, only the elimination process is important, which in this case is nonlinear.
Based on Model B and the computable criterion (15) it can easily be checked whether the condition for the applicability of Model C are fulfilled. This has been demonstrated for the EGF/EGFR system in section “RME for the EGF/EGFR system”, see Figs. 4 and 5.
The reduced models are derived under the quasisteady state assumption that the receptor redistribution processes are much faster than the ligand pharmacokinetics. This assumption is of the same type as the assumption underlying the Michaelis–Menten model of enzyme reactions, where it is assumed that the complex formation, dissociation and catalytic transformation are much faster than the transformation of substrate into product. In order to finally derive reduced models, we have to make an additional assumption on the timescale of receptor synthesis and degradation. There are three different scenarios: receptor synthesis and degradation is (i) as fast as receptor redistribution (or faster); (ii) slower than the time scale of ligand pharmacokinetics; or (iii) at an intermediate time scale, i.e., comparable or faster than ligand PK but slower than receptor redistribution. The first two scenarios correspond to our fast and slow scenario. Under these assumptions it is possible to either treat receptor synthesis and degradation the same way as the redistribution processes (in the fast scenario) or neglect it and treat the total amount of receptor as a constant (in the slow scenario), since in the latter it would not impact the total number of receptors on the time scale of interest. In the third scenario, however, receptor synthesis and degradation would need to be taken into account in terms of an additional ODE. Unless further assumptions are made, this would require to consider the full system of Eqs. 1–5—which is not suitable for PK parameter estimation in clinical trials.
The elimination process of RME is specified in terms of the parameters V _{max} and K _{ M }. Noteworthy, the maximal elimination rate V _{max} is independent of the processes of complex formation (k _{on}) and dissociation (k _{off}) of the receptorligand complex. However, the parameters k _{on} and k _{off} influence the amount of extracellular ligand molecules K _{ M }, at which the elimination rate is halfmaximal.
In Fig. 6, we studied the impact of different internalization rate constants k _{interRL} on RME. An altered k _{interRL} could, e.g., result from a mutation in the EGF receptor, as it has been observed experimentally [37]. Our analysis in section “Nonlinear PK caused by RME” shows that the ligand elimination rate is affected by various processes inside the cell. For example, the elimination rate decreases with decreasing complex internalization rate constant, but the difference is much less pronounced for a ligand with decreased association and dissociation rate constants k _{on} and k _{off}—even though the dissociation constant K _{ D } is the same in both scenarios (see Fig. 6, left vs. right). From the detailed Model A, this phenomenon is understandable: given a ligand that forms a complex with rate constant k _{on}, once the ligand–receptor complex is formed at the membrane, its fate is a balance between dissociation (specified in terms of k _{off}) and internalization (specified in terms of k _{interRL}). If, e.g., k _{off}/k _{interRL} ≪ 1 then the complex will predominantly be internalized. Based on K _{ D } alone, this property of receptor systems can not be observed. The ratio k _{off}/k _{interRL} has recently been introduced as one of two key parameters to characterize different cell surface receptor systems (termed the consumption parameter) [28]. In general, our analysis shows that reduced ligand elimination from the extracellular space can be due to altered processes inside the cell other than the velocity of internalization of the complex. The influences of the processes can be deduced from Eq. 28 and is summarized in Table 1. The nonlinearity increases with parameters that accelerate’ the processes of receptor availability at the surface (R _{0}, k _{recyR}) or that accelerate’ the transport and intracellular degradation of extracellular ligand (k _{on}, k _{interRL}, k _{lyso}). Counteracting processes (related to the parameters k _{off}, k _{interR}, k _{recyRL}) decrease the extent of nonlinearity.
Targetmediated drug disposition (TMDD) models explicitly account for binding to a target and potential degradation of the resulting complex [16, 17, 18]. Although originally developed to describe effects of extensive drug target binding in tissues, TMDD has more recently also gained interest as a model of saturable elimination mechanisms for specific peptide and protein drugs, including RME [6, 18, 19]. Between TMDD and the herein presented approach, there are a number of distinct differences. First, the TMDD approach considers pharmacological target binding as the key process controlling the complex nonlinear processes. Particular features of receptor trafficking inside the cell are not taken into account. Second, whenever a drug molecule is degraded in the TMDD setting, both, a drug and a receptor molecule are degraded. In the herein presented approach, degradation of the drug does not necessarily imply degradation of the receptor, since the receptor can be recycled. This is, e.g., an important characteristics for the ligand TNFα. Third, in [16], a reduced model of TMDD is presented based on a equilibrium assumption. In this reduced TMDD model, the unbound extracellular drug concentration is a function of the total concentration, the total receptor concentration R _{tot} and the equilibrium dissociation constant K _{ D } [16, Eq. 11]. In our reduced model B, in contrast, the extracellular drug concentration is a function of the total concentration, the maximal receptor binding capacity B _{max} and the quasisteady state parameter K _{ M } (cf. Eq. 8). As a consequence, the models make qualitatively different predictions. For instance, K _{ M } does not only depend on the ratio of k _{off} and k _{on} (i.e., K _{ D }), but also on the actual magnitude of the two parameters, in addition to the dependence on receptor systems parameters. This implies that two drugs with the same K _{ D } but different k _{off} values might be impacted by RME very differently. This has been illustrated in Fig. 6 (compare left and right graphics) and discussed above.
 Consistency check: Use both reduced Models B and C to fit the data and check the two conditions (15) and (17):A violation of the conditions might indicate an insufficient experimental design, and/or insufficient convergence of the fitting algorithm (local minimum).$$ \frac{B_{\rm max}} {K_M + L_{\rm ex}} \ll 1\quad \hbox{and}\quad V_{\rm max}=k_{\rm deg}\cdot B_{\rm max}. $$(33)
Different empirical models have been proposed and used to model the nonlinear pharmacokinetics of therapeutic proteins [7, 8, 9, 10, 11, 12, 13, 14, 15]. While, e.g., a Michaelis–Menten based RME model as part of a PK model allowed for describing data in one PK data analysis (e.g., [10]), it failed to do so in another (e.g., [11]). Due to lack of a sound theoretical basis to understand the different performances of empirical models, this certainly was an unsatisfactory situation. The herein presented analysis gives a thorough background of RME and a clear rationale as to when the proposed reduced models are applicable. In addition, the functional relations between the parameters of the detailed Model A and the reduced Models B and C might also serve as a first step to scale in vitro observations on RME to in vivo predictions of either target mediated disposition or Michaelis–Menten elimination, dependent upon the expression level and turnover of the target.
Footnotes
 1.
The originally published equations in [11, Supplement] are identical to a certain discretization of the system of ODEs (30)–(32). The advantage of stating the system as continuous ODEs is that subsequently any numerical scheme can be used to solve them, in particular high accuracy ODE solver with adaptive step size control.
Notes
Acknowledgements
The authors are grateful to the reviewers for their valuable comments on the manuscript.
Open Access
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Supplementary material
References
 1.Meibohm B (2006) Pharmacokinetics and pharmacodynamics of biotech drugs. WileyVCH Verlag, WeinheimCrossRefGoogle Scholar
 2.Kuester K, Kloft C (2006) Pharmacokinetics of monoclonal antibodies. In Meibohm B (ed) Pharmacokinetics and pharmacodynamics of biotech drugs, chapter 3. WileyVCH Verlag, Weinheim, pp 45–91Google Scholar
 3.Mahmood I, Green MD (2005) Pharmacokinetic and pharmacodynamic considerations in the development of therapeutic proteins. Clin Pharmacokinet 44:331–347PubMedCrossRefGoogle Scholar
 4.RussellJones GJ (2001) The potential use of receptormediated endocytosis for oral drug delivery. Adv Drug Deliver Rev 46:59–73CrossRefGoogle Scholar
 5.Sorkin A, Von Zastrow M (2002) Signal transduction and endocytosis: close encounters of many kinds. Nat Rev Mol Cell Biol 3:600–614PubMedCrossRefGoogle Scholar
 6.Tang L, Persky A, Hochhaus G, Meibohm B (2004) Pharmacokinetic aspects of biotechnology products. J Pharm Sci 93:21842204PubMedCrossRefGoogle Scholar
 7.Dirks NL, Nolting A, Kovar A, Meibohm B (2008) Population pharmacokinetics of cetuximab in patients with squamous cell carcinoma of the head and neck. J Clin Pharmacol 48:267–278PubMedCrossRefGoogle Scholar
 8.Kuester K, Kovar A, Lüpfert C, Brockhaus B, Kloft C (2008) Population pharmacokinetic data analysis of three phase I studies of matuzumab, a humanised antiEGFR monoclonal antibody in clinical cancer development. Br J Cancer 98:900–906PubMedCrossRefGoogle Scholar
 9.Mould DR, Sweeney KRD (2007) The pharmacokinetics and pharmacodynamics of monoclonal antibodies—mechanistic modeling applied to drug development. Curr Opin Drug Discov Dev 10:84–96Google Scholar
 10.Kloft C, Graefe EU, Tanswell P, Scott AM, Hofheinz R, Amelsberg A, Karlsson MO (2004) Population pharmacokinetics of sibrotuzumab, a novel therapeutic monoclonal antibody, in cancer patients. Invest New Drugs 22:39–52PubMedCrossRefGoogle Scholar
 11.Lammerts van Bueren JJ, Bleeker WK, Bøgh HO, Houtkamp M, Schuurman J, van de Winkel JGJ, Parren PWHI (2006) Effect of target dynamics on pharmacokinetics of a novel therapeutic antibody against the epidermal growth factor receptor: implications for the mechanisms of action. Cancer Res 66:7630–7638CrossRefGoogle Scholar
 12.Ette EI, Williams PJ, Ho Kim Y, Lane JR, Liu MJ, Capparelli EV (2003) Model appropriateness and population pharmacokinetic modeling. J Clin Pharmacol 43:610–623PubMedGoogle Scholar
 13.Sheiner LB, Beal SL (1983) Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: routine clinical pharmacokinetic data. J Pharmacokinet Biopharm 11:303–319PubMedCrossRefGoogle Scholar
 14.Sheiner BL, Beal SL (1981) Evaluation of methods for estimating population pharmacokinetic parameters. II. Biexponential model and experimental pharmacokinetic data. J Pharmacokinet Biopharm 9:635–651PubMedCrossRefGoogle Scholar
 15.Sheiner LB, Beal SL (1980) Evaluation of methods for estimating population pharmacokinetics parameters. I. Michaelis–Menten model: routine clinical pharmacokinetic data. J Pharmacokinet Biopharm 8:553–571PubMedCrossRefGoogle Scholar
 16.Mager DE, Krzyzanski W (2005) Quasiequilibrium pharmacokinetic model for drugs exhibiting targetmediated drug disposition. Pharm Res 22:1589–1596PubMedCrossRefGoogle Scholar
 17.Mager DE, Jusko WJ (2001) General pharmacokinetic model for drugs exhibiting targetmediated drug disposition. J Pharmacokinet Pharmacodyn 28:507–532PubMedCrossRefGoogle Scholar
 18.Mager DE (2006) Targetmediated drug disposition and dynamics. Biochem Pharmacol 72:1–10PubMedCrossRefGoogle Scholar
 19.Lobo ED, Hansen RJ, Balthasar JP (2004) Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci 93:2645–2668PubMedCrossRefGoogle Scholar
 20.Hendriks BS, Orr G, Wells A, Wiley HS, Lauffenburger DA (2005) Parsing ERK activation reveals quantitatively equivalent contributions from epidermal growth factor receptor and HER2 in human mammary epithelial cells. J Biol Chem 280:6157–6169PubMedCrossRefGoogle Scholar
 21.Wiley HS, Shvartsman SY, Lauffenburger DA (2003) Computational modeling of the EGFreceptor system: a paradigm for systems biology. Trends Cell Biol 13:43–50PubMedCrossRefGoogle Scholar
 22.Sarkar CA and Lauffenburger DA (2003) Celllevel pharmacokinetic models of granulocyte colonystimulating factor: implications for ligand lifetime and potency in vivo. Mol Pharmacol 63:147–158PubMedCrossRefGoogle Scholar
 23.De Luca A, Carotenuto A, Rachiglio A, Gallo M, Maiello MR, Aldinucci D, Pinto A, Normanno N (2008) The role of the EGFR signaling in tumor microenvironment. J Cell Physiol 214:559–567PubMedCrossRefGoogle Scholar
 24.Watanabe T, Shintani A, Nakata M, Shing Y, Folkman J, Igarashi K, Sasada R (1994) Recombinant human betacellulin. Molecular structure, biological activities, and receptor interaction. J Biol Chem 269:9966–9973PubMedGoogle Scholar
 25.Wells A (1999) EGF receptor. Int J Biochem Cell Biol 31:637–643PubMedCrossRefGoogle Scholar
 26.Harari PM (2004) Epidermal growth factor receptor inhibition strategies in oncology. Endocr Relat Cancer 11:689–708PubMedCrossRefGoogle Scholar
 27.Wiley HS, Cunningham DD (1981) A steady state model for analyzing the cellular binding, internalization and degradation of polypeptide ligands. Cell 25:433–440PubMedCrossRefGoogle Scholar
 28.Shankaran H, Resat H, Wiley HS (2007) Cell surface receptors for signal transduction and ligand transport: a design principles study. PLoS Comput Biol 3:e101PubMedCrossRefGoogle Scholar
 29.Baselga J (2001) The EGFR as a target for anticancer therapy—focus on cetuximab. Eur J Cancer 37(Suppl 4):S16–S22PubMedCrossRefGoogle Scholar
 30.Baselga J (2002) Why the epidermal growth factor receptor? The rationale for cancer therapy. Oncologist 7(Suppl 4):2–8PubMedCrossRefGoogle Scholar
 31.Baselga J (2000) New therapeutic agents targeting the epidermal growth factor receptor. J Clin Oncol 18:54S–59SPubMedGoogle Scholar
 32.Goel S, Mani S, PerezSoler R (2002) Tyrosine kinase inhibitors: a clinical perspective. Curr Oncol Report 4:9–19CrossRefGoogle Scholar
 33.Raymond E, Faivre S, Armand JP (2000) Epidermal growth factor receptor tyrosine kinase as a target for anticancer therapy. Drugs 60(1):15–23; discussion 41–2PubMedCrossRefGoogle Scholar
 34.Hendriks BS, Opresko LK, Wiley HS, Lauffenburger DA (2003) Coregulation of epidermal growth factor receptor/human epidermal growth factor receptor 2 (HER2) levels and locations: quantitative analysis of HER2 overexpression effects. Cancer Res 63:1130–1137PubMedGoogle Scholar
 35.Bleeker WK, Lammerts van Bueren JJ, van Ojik HH, Gerritsen AF, Pluyter M, Houtkamp M, Halk E, Goldstein J, Schuurman J, van Dijk MA, van de Winkel JGJ, Parren PWHI (2004) Dual mode of action of a human antiepidermal growth factor receptor monoclonal antibody for cancer therapy. J Immunol 173:4699–4707PubMedGoogle Scholar
 36.Bastholt L, Specht L, Jensen K, Brun E, Loft A, Petersen J, Kastberg H, Eriksen JG (2007) Phase I/II clinical and pharmacokinetic study evaluating a fully human monoclonal antibody against EGFr (HuMaxEGFr) in patients with advanced squamous cell carcinoma of the head and neck. Radiother Oncol 85:24–28PubMedCrossRefGoogle Scholar
 37.Wells A, Welsh JB, Lazar CS, Steven Wiley H, Gill GN, Rosenfeld MG (1990) Ligandinduced transformation by a noninternalizing epidermal growth factor receptor. Science 247:962–964PubMedCrossRefGoogle Scholar
 38.Reddy CC, Wells A, Lauffenburger DA (1994) Proliferative response of fibroblasts expressing internalizationdeficient epidermal growth factor (EGF) receptors is altered via differential EGF depletion effect. Biotechnol Prog 10:377–384PubMedCrossRefGoogle Scholar
 39.Godfrey K (1983) Compartmental models and their application. Academic Press, LondonGoogle Scholar