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Impact of altered endogenous IgG on unspecific mAb clearance

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Abstract

Immunodeficient mice are crucial models to evaluate the efficacy of monoclonal antibodies (mAbs). When studying mAb pharmacokinetics (PK), protection from elimination by binding to the neonatal Fc receptor (FcRn) is known to be a major process influencing the unspecific clearance of endogenous and therapeutic IgG. The concentration of endogenous IgG in immunodeficient mice, however is reduced, and this effect on the FcRn protection mechanism and subsequently on unspecific mAb clearance is unknown, yet of great importance for the interpretation of mAb PK data. We used a PBPK modelling approach to elucidate the influence of altered endogenous IgG concentrations on unspecific mAb clearance. To this end, we used PK data in immunodeficient mice, i.e. nude and severe combined immunodeficiency mice. To avoid impact of target-mediated clearance processes, we focussed on mAbs without affinity to a target antigen in these mice. In addition, intravenous immunoglobulin (IVIG) data of immunocompetent mice was used to study the impact of increased total IgG concentrations on unspecific therapeutic antibody clearance. The unspecific clearance is linear, whenever therapeutic IgG concentrations, i.e. mAb and IVIG concentrations are lower than FcRn; it can be non-linear if therapeutic IgG concentrations are larger than FcRn and endogenous IgG concentrations (e.g., under IVIG therapy). Unspecific mAb clearance of immunodeficient mice is effectively linear (under mAb doses as typically used in human). Studying the impact of reduced endogenous IgG concentrations on unspecific mAb clearance is of great relevance for the extrapolation to clinical species, e.g., when predicting mAb PK in immunosuppressed cancer patients.

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References

  1. Dirks NL, Meibohm B (2010) Population pharmacokinetics of therapeutic monoclonal antibodies. Clin Pharmacokinet 49(10):633–659

    Article  CAS  PubMed  Google Scholar 

  2. Mager DE, Jusko WJ (2001) General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J Pharmacokinet Pharmacodyn 28(6):507–532

    Article  CAS  PubMed  Google Scholar 

  3. Mager DE, Krzyzanski W (2005) Quasi-equilibrium pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Pharm Res 22(10):1589–96

    Article  CAS  PubMed  Google Scholar 

  4. Gibiansky L, Gibiansky E, Kakkar T, Ma P (2008) Approximations of the target-mediated drug disposition model and identifiability of model parameters. J Pharmacokinet Pharmacodyn 35(5):573–591

    Article  CAS  PubMed  Google Scholar 

  5. Grimm HP (2009) Gaining insights into the consequences of target-mediated drug disposition of monoclonal antibodies using quasi-steady-state approximations. J Pharmacokinet Pharmacodyn 36(5):407–420

    Article  CAS  PubMed  Google Scholar 

  6. Krippendorff BF, Kuester K, Kloft C, Huisinga W (2009) Nonlinear pharmacokinetics of therapeutic proteins resulting from receptor mediated endocytosis. J Pharmacokinet Pharmacodyn 36(3):239–260

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Krippendorff BF, Oyarzún DA, Huisinga W (2012) Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modelling. J Pharmacokinet Pharmacodyn 39(2):125–139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Keizer RJ, Huitema ADR, Schellens JHM, Beijnen JH (2010) Clinical pharmacokinetics of therapeutic monoclonal antibodies. Clin Pharmacokinet 49(8):493–507

    Article  CAS  PubMed  Google Scholar 

  9. Ferl GZ, Wu AM, DiStefano JJ (2005) A predictive model of therapeutic monoclonal antibody dynamics and regulation by the neonatal Fc receptor (FcRn). Ann Biomed Eng 33(11):1640–1652

    Article  PubMed  Google Scholar 

  10. Garg A, Balthasar JP (2007) Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn 34(5):687–709

    Article  CAS  PubMed  Google Scholar 

  11. Chen Y, Balthasar JP (2012) Evaluation of a catenary PBPK model for predicting the in vivo disposition of mAbs engineered for high-affinity binding to FcRn. AAPS J 14(4):850–859

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jones HM, Mayawala K, Poulin P (2012) Dose selection based on physiologically based pharmacokinetic (PBPK) approaches. AAPS J 15(2):377–387

    Article  PubMed  PubMed Central  Google Scholar 

  13. Cao Y, Balthasar JP, Jusko WJ (2013) Second-generation minimal physiologically-based pharmacokinetic model for monoclonal antibodies. J Pharmacokinet Pharmacodyn 40(5):597–607

    Article  CAS  PubMed  Google Scholar 

  14. Fronton L, Pilari S, Huisinga W (2014) Monoclonal antibody disposition: a simplified PBPK model and its implications for the derivation and interpretation of classical compartment models. J Pharmacokinet Pharmacodyn 41(2):87–107

    Article  CAS  PubMed  Google Scholar 

  15. Huisinga W, Fuhrmann S, Fronton L, Krippendorff BF (2015) Target-driven pharmacokinetics of biotherapeutics. In: Zhou H, Theil FP (eds) Application of ADME and translational PK/PD in the development of therapeutic drugs. Wiley, New York, pp 197–209

    Google Scholar 

  16. Shah DK, Betts AM (2013) Antibody biodistribution coefficients Inferring tissue concentrations of monoclonal antibodies based on the plasma concentrations in several preclinical species and human. mAbs 5(2):297–305

    Article  PubMed  PubMed Central  Google Scholar 

  17. Jones HM (2013) Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacomet Syst Pharmacol 2(e63):1–2

    Google Scholar 

  18. Kim R, Emi M, Tanabe K (2006) Cancer immunosuppression and autoimmune disease: beyond immunosuppressive networks for tumour immunity. Immunology 119(2):254–264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. von Bernstorff W, Voss M, Freichel S, Schmid A, Vogel I, Jöhnk C, Henne-Bruns D, Kremer B, Kalthoff H (2001) Systemic and local immunosuppression in pancreatic cancer patients. Clin Cancer Res 7(3):925s–932s

    Google Scholar 

  20. Penn I, Starzl TE (1973) Immunosuppression and cancer. Transplant Proc 5(1):943–947

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Wochner RD, Drews G, Strober W, Waldmann TA (1966) Accelerated breakdown of immunoglobulin G (IgG) in myotonic dystrophy: a hereditary error of immunoglobulin catabolism. J Clin Invest 45(3):321–329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Schuppan D, Afdhal NH (2008) Liver cirrhosis. Lancet 371:838–851

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kawai R, Lemaire M, Steimer JL, Bruelisauer A, Niederberger W, Rowland M (1994) Physiologically based pharmacokinetic study on a cyclosporin derivative, SDZ IMM 125. J Pharmacokinet Biopharm 22(5):327–365

    Article  CAS  PubMed  Google Scholar 

  24. Boswell CA, Mundo EE, Ulufatu S, Bumbaca D, Cahaya HS, Majidy N, Hoy MV, Schweiger MG, Fielder PJ, Prabhu S, Khawli LA (2014) Comparative physiology of mice and rats: radiometric measurement of vascular parameters in rodent tissues. Mol Pharm 11:1591–1598

    Article  CAS  PubMed  Google Scholar 

  25. Xiao JJ (2012) Pharmacokinetic models for FcRn-mediated IgG disposition. J Biomed Biotechnol. doi:10.1155/2012/282989

  26. Garg A (2007) Investigation of the role of FcRn in the absorption, distribution, and elimination of monoclonal antibodies. Dissertation, State University of New York at Buffalo

  27. Abuqayyas L, Balthasar JP (2012) Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer. J Pharmacokinet Pharmacodyn 39(6):683–710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Envigo (2015) SCID Mice | Envigo. http://www.envigo.com/products-services/research-models-services/research-models/mice/mutant/scid-mice/c.b-17-icrhsd-prkdcscid/. Accessed 28 September 2014

  29. Baxter LT, Zhu H, Mackensen DG, Jain RK (1994) Physiologically based pharmacokinetic model for specific and nonspecific monoclonal antibodies and fragments in normal tissues and human tumor xenografts in nude mice. Cancer Res 54(6):1517–1528

    CAS  PubMed  Google Scholar 

  30. Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP (1997) Physiological parameter values for physiologically based pharmacokinetic models. Toxicol Ind Health 13(4):407–484

    Article  CAS  PubMed  Google Scholar 

  31. Diehl KH, Hull R, Morton D, Pfister R, Rabemampianina Y, Smith D, Vidal JM, Van De Vorstenbosch C (2001) A good practice guide to the administration of substances and removal of blood, including routes and volumes. J Appl Toxicol 21:15–23

    Article  CAS  PubMed  Google Scholar 

  32. Windberger U, Bartholovitsch A, Plasenzotti R, Korak KJ, Heinze G (2003) Whole blood viscosity, plasma viscosity and erythrocyte aggregation in nine mammalian species: reference values and comparison of data. Exp Physiol 88:431–440

    Article  CAS  PubMed  Google Scholar 

  33. Baxter LT, Zhu H, Mackensen DG, Butler WF, Jain RK (1995) Biodistribution of monoclonal antibodies : scale-up from mouse to human using a physiologically based pharmacokinetic model. Cancer Res 55:4611–4622

    CAS  PubMed  Google Scholar 

  34. Junghans RP, Anderson CL (1996) The protection receptor for IgG catabolism is the beta2-microglobulin-containing neonatal intestinal transport receptor. Proc Natl Acad Sci USA 93:5512–5516

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Bloemmen J, Eyssen H (1973) Immunoglobulin levels of sera of genetically thymusless (nude) mice. Eur J Immunol 3:117–118

    Article  CAS  PubMed  Google Scholar 

  36. Deng R, Meng YG, Hoyte K, Lutman J, Lu Y, Iyer S, Deforge LE, Theil F, Fielder PJ, Prabhu S (2012) mAbs. Subcutaneous bioavailability of therapeutic antibodies as a function of FcRn binding affinity in mice 4(1):101–109

    Google Scholar 

  37. Covell DG, Barbet J, Holton OD, Black CDV, Parker RJ, Weinstein JN (1986) Pharmacokinetics of monoclonal immunoglobulin G1, F(ab’)2, and Fab in mice. Cancer Res 46:3969–3978

    CAS  PubMed  Google Scholar 

  38. El-Masri HA, Portier CJ (1998) Physiologically based pharmacokinetics model of primidone and its metabolites phenobarbital and phenylethylmalonamide in humans, rats and mice. Drug Metab Dispos 26:585–594

    CAS  PubMed  Google Scholar 

  39. Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM J Optim 9(1):112–147

    Article  Google Scholar 

  40. Kreutz C, Raue A, Kaschek D, Timmer J (2013) Profile likelihood in systems biology. FEBS J 280(11):2564–2571

    Article  CAS  PubMed  Google Scholar 

  41. 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(1):39–52

    Article  CAS  PubMed  Google Scholar 

  42. Gill KL, Machavaram KK, Rose RH, Chetty M (2016) Potential sources of inter-subject variability in monoclonal antibody pharmacokinetics. Clin Pharmacokinet 55(7):789–805

    Article  CAS  PubMed  Google Scholar 

  43. Fan YY, Neubert H (2016) Quantitative analysis of human neonatal Fc receptor (FcRn) tissue expression in transgenic mice by online peptide immuno-affinity LC-HRMS. Anal Chem 88(8):4239–4247

    Article  CAS  PubMed  Google Scholar 

  44. Waldmann TA, Terry WD (1990) Familial hypercatabolic hypoproteinemia. A disorder of endogenous catabolism of albumin and immunoglobulin. J Clin Invest 86(6):2093–2098

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhou J, Johnson JE, Ghetie V, Ober RJ, Ward ES (2003) Generation of mutated variants of the human form of the MHC class I-related receptor, FcRn, with increased affinity for mouse immunoglobulin G. J Mol Biol 332(03):901–913

    Article  CAS  PubMed  Google Scholar 

  46. Popov S, Hubbard JG, Kim JK, Ober B, Ghetie V, Ward ES (1996) The stoichiometry and affinity of the interaction of murine Fc fragments with the MHC class I-related receptor, FcRn. Mol Immunol 33(6):521–530

    Article  CAS  PubMed  Google Scholar 

  47. Ober RJ, Radu CG, Ghetie V, Ward ES (2001) Differences in promiscuity for antibody-FcRn interactions across species: implications for therapeutic antibodies. Int Immunol 13(12):1551–1559

    Article  CAS  PubMed  Google Scholar 

  48. Gurbaxani B, Dostalek M, Gardner I (2013) Are endosomal trafficking parameters better targets for improving mAb pharmacokinetics than FcRn binding affinity? Mol Biol 56(4):660–674

    CAS  Google Scholar 

  49. Stoop J, Zegers B (1969) Serum immunoglobulin levels in healthy children and adults. Clin Exp Immunol 4:101–112

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Abdiche YN, Yeung YA, Chaparro-Riggers J, Barman I, Strop P, Chin SM, Pham A, Bolton G, McDonough D, Lindquist K, Pons J, Rajpal A (2015) The neonatal Fc receptor (FcRn) binds independently to both sites of the IgG homodimer with identical affinity. mAbs 7(2):331–343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Suzuki T, Ishii-Watabe A, Tada M, Kobayashi T, Kanayasu-Toyoda T, Kawanishi T, Yamaguchi T (2010) Importance of neonatal FcR in regulating the serum half-life of therapeutic proteins containing the Fc domain of human IgG1: a comparative study of the affinity of monoclonal antibodies and Fc-fusion proteins to human neonatal FcR. J Immunol 184(4):1968–1976

    Article  CAS  PubMed  Google Scholar 

  52. Dostalek M, Gardner I, Gurbaxani BM, Rose RH, Chetty M (2013) Pharmacokinetics, pharmacodynamics and physiologically-based pharmacokinetic modelling of monoclonal antibodies. Clin Pharmacokinet 52:83–124

    Article  CAS  PubMed  Google Scholar 

  53. Yip V, Palma E, Tesar DB, Mundo EE, Bumbaca D, Torres EK, Reyes NA, Shen BQ, Fielder PJ, Prabhu S, Khawli LA, Boswell CA (2014) Quantitative cumulative biodistribution of antibodies in mice: effect of modulating binding affinity to the neonatal Fc receptor. mAbs 6(3):689–696

    Article  PubMed  PubMed Central  Google Scholar 

  54. Akilesh S, Christianson GJ, Roopenian DC, Shaw AS (2007) Neonatal FcR expression in bone marrow-derived cells functions to protect serum IgG from catabolism. J Immunol 179(7):4580–4588

    Article  CAS  PubMed  Google Scholar 

  55. Swiercz R, Mo M, Khare P, Schneider Z, Ober RJ, Ward ES (2016) Loss of expression of the recycling receptor, FcRn, promotes tumor cell growth by increasing albumin consumption. Oncotarget 8(2):3528–3541

    PubMed Central  Google Scholar 

  56. Gullino PM, Grantham FH, Smith SH (1965) The interstitial water space of tumors the interstitial water space of tumors. Cancer Res 25:727–731

    CAS  PubMed  Google Scholar 

  57. Urva SR, Yang VC, Balthasar JP (2010) Physiologically based pharmacokinetic model for T84. 66: a monoclonal anti-CEA antibody. J Pharm Sci 99(3):1582–1600

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

S.F. acknowledges fruitful discussions with Hans Peter Grimm (F. Hoffmann-La Roche Ltd, Basel, Switzerland).

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Correspondence to Wilhelm Huisinga.

Appendix

Appendix

Extension of the generic PBPK model to account for FcRn-dependent clearance kinetically linked to interstitial fluid of organs

In order to account for FcRn-dependent clearance kinetically linked to interstitial fluid of organs, we make use of the organ extraction ratio \(E_{\text{org}}\). The extraction ratio is estimated for two groups of organs, dependent on the tightness and leakiness of the vessel wall. Intrinsic clearance from interstitial fluid of organs \(\text{CLint}_{\text{int}}\) is determined via tissue extraction ratio.

Linear clearance from plasma (\({\text{CLpla}}\)) is calculated via total clearance (\(\text{CL}_{\text{tot}}\)) and the plasma clearance corresponding to the different intrinsic clearances from the organs as

$$\begin{aligned} {\text{CLpla}} & = \text{CL}_{\text{tot}}- \sum _{\text{tight}\ne {\text{pla}}} E_{\text{org}}\cdot L_{\text{org}}\cdot (1-\sigma _{\text{vas}}) \\ &\quad + \sum _{\text{leaky}\ne {\text{pla}}} E_{\text{org}}\cdot L_{\text{org}}\cdot (1-\sigma _{\text{vas}}). \end{aligned}$$
(18)

FcRn concentration [nM] in interstitial fluid was estimated for two groups of tissues. Fraction unbound \({\text{fu}}_{{\text{IgG}}}\) is calculated dependent on total IgG concentration (i.e. therapeutic IgG and endogenous IgG) and FcRn concentration, both in interstitial fluid of the organs. \({\text{fu}}_{{\text{IgG}}}\) is determined by one of the methods described in section ‘Theoretical’.

Hence, intrinsic clearance is calculated based on total IgG:

$$\begin{aligned} \text{CLint}= \text{CLint}_{\text{u}}\cdot {\text{fu}}_{{\text{IgG}}}. \end{aligned}$$
(19)

Endogenous IgG synthesis rate \(k_{\text{syn}}\) in [nmol/min] is determined via clearance processes (from plasma and interstitial fluid of organs) and baseline (steady state) endogenous plasma concentration \({\text{IgG}}_{{\text{endo}},{\text{baseline}}}\):

$$\begin{aligned} k_{\text{syn}}= ({\text{CLpla}}+ \sum \text{CLint}_{\text{int}}\cdot {\text{ABC}}_{\text{int}}) \cdot {\text{IgG}}_{{\text{endo}},{\text{baseline}}}. \end{aligned}$$
(20)

Baseline concentration of endogenous IgG in interstitial fluid is computed from \({\text{IgG}}_{{\text{endo}},{\text{baseline}}}\) via \({\text{ABC}}_{\text{int}}\):

$$\begin{aligned} {\text{IgG}}_{{\text{endo}},{\text{baseline}},{\text{int}}}={\text{IgG}}_{{\text{endo}},{\text{baseline}}} \cdot {\text{ABC}}_{\text{int}}. \end{aligned}$$
(21)

Total \({\text{CLpla}}\) is estimated from the the PBPK model in Eqs. (1)–(2) with constant \({\text{CLpla}}\) and with extraction ratios set to zero (see Table 6). Then, the herein presented PBPK model was used to estimate the organ extraction ratios and FcRn concentrations for two groups of tissues. As already explained in the Discussion, predictions for mAb PK in FcRn wild-type mice, nude and SCID mice with FcRn salvage kinetically linked to plasma are indistinguishable to predictions based on the herein presented extension for FcRn linked to the two groups of organs.

Modelling FcRn-dependent clearance with almost tenfold difference in binding affinity of murine and human IgG to mouse FcRn

Zhou et al. [45] report an almost tenfold difference in binding affinity with higher affinity of human IgG than murine IgG to mouse FcRn (\({\text{hK}}_{\text{D}}\) = 82 nM versus \({\text{mK}}_{\text{D}}\) = 750 nM, respectively). We used the equilibrium binding model with different \({\text{K}}_{\text{D}}\) values to predict the influence of human IgG (IVIG) on murine IgG (murine mAb and endogenous IgG) clearance in FcRn wild-type mice. The model cannot describe quantitatively the experimentally measured mAb concentrations following IVIG therapy resulting in a too strong protection mechanism of FcRn for IVIG, see Figure 12. However qualitatively it predicts the influence of IVIG on murine IgG clearance and endogenous IgG concentrations very well. Following a higher binding affinity of IVIG for mouse FcRn, IVIG is more protected from elimination than murine IgG resulting in a lower IVIG clearance. After a significant decrease in endogenous IgG concentration, it takes time for endogenous IgG to return to baseline plasma concentration. This can be explained with the almost tenfold lower binding affinity of murine IgG for mouse FcRn resulting in a less protection by FcRn and a higher degradation within endo-lysosomes (see Figure 13 for kinetics of endogenous IgG and IVIG predicted by the model with twofold difference in \({\text{K}}_{\text{D}}\)).

Fig. 12
figure 12

mAb plasma concentration-time profiles predicted by the PBPK model with equilibrium binding with approx. tenfold difference in \({\text{K}}_{\text{D}}\) values (solid lines) compared to experimentally measured concentrations in wild-type mice after i.v. bolus administration of 8 mg/kg 7E3 (blue) following different doses of IVIG (from top to down): 0.4 g/kg IVIG (red), 1g/kg IVIG (black) and 2g/kg IVIG (green) (Color figure online)

Fig. 13
figure 13

Simulated IVIG plasma concentration-time profiles and endogenous IgG plasma concentration-time profiles. Simulations are based on the PBPK model with equilibrium binding with twofold difference in \({\text{K}}_{\text{D}}\) values and refer to wild-type mice after i.v. bolus administration of 8 mg/kg 7E3 (blue) following different doses of IVIG: 0.4 g/kg IVIG (red), 1g/kg IVIG (black) and 2g/kg IVIG (green) (Color figure online)

Accounting for non-equilibrium binding effects on unspecific clearance

So far, we assume equilibrium binding with \({\text{K}}_{\text{D}} = ({\text{k}}_{\text{off}}/{\text{k}}_{\text{on}})\) to model FcRn–IgG interaction. Non-equilibrium binding with baseline rate of \({\text{fu}}_{{\text{IgG}}}\) > 0 may be explainable by a very slow and incomplete binding of total IgG to FcRn leading to a high amount of total IgG in lysosome or unregulated sorting resulting in FcRn within lysosomes. We used a non-equilibrium binding model (see Eq. (22)) and jointly estimated an unbound plasma clearance, total FcRn and a baseline clearance \(\text{CL}_\text{base}\) to predict mAb PK data in FcRn wild-type mice following IVIG treatment, nude mice and SCID mice. Following non-equilibrium binding, we obtained estimates for total FcRn and unbound plasma clearance, that are comparable for wild-type mice and immunodeficient mice with a baseline fraction unbound \({\text{fu}}_{\text{base}}\) of approx. 11% (data not shown).

To account for non-equilibrium binding, we parameterised the unspecific plasma clearance \({\text{CLpla}}\) in the model with equilibrium binding affinity (see Eq. (4)) in addition with a baseline clearance \({\text{CL}}_{\text{base}}\) as:

$$\begin{aligned} {\text{CLpla}}= {\text{CLpla}}_{\text{u}}\cdot {\text{fu}}_{{\text{IgG}}} + \text{CL}_\text{base} \end{aligned}$$
(22)

with \(\text{CL}_\text{base}\)= \({\text{fu}}_\text{base}\) \(\cdot\) \({\text{CLpla}}_{\text{u}}\).

Presence of tumour has no influence on mAb PK

Total tumour volumes (including vascular space) for nude mice were taken from [29] to be 0.472 mL and for SCID mice to be 0.250 mL based on [27]. We used the values reported in [56] to determine the tissue volume fraction of total tumour volume \((f_{\text{tis}} = 0.77)\), the vascular volume fraction of total tumour volume \((f_{\text{vas}}=0.06)\) and the interstitial volume fraction of tumour tissue volume \((f_{\text{int}}=0.42)\). Vascular volume was included in the blood volume \(V_{\text{blo}}\). Tumour growth was modelled using the estimated tumour growth rate \({\text{k}}_{\text{growth}}\) for LS174T tumour of approx. \(\text{6.35e-5}\) 1/min in [57]. Total tumour volumes were used as initial tumour volumes for the two mouse strains. Tumour plasma flow was set to 0.1 L/min as measured in [29] and tumour lymph flow rate was assumed to be 0.4% of tumour plasma flow. The two remaining unknown parameter values for the tumour, i.e., vascular reflection coefficient \(\sigma _{\text{vas}}\) and the antibody biodistribution coefficient \({\text{ABC}}_\text{tum}\) were estimated. The two parameters were estimated separately for nude and SCID mice due to differences in tumour cell lines and tumour inoculation. The generic PBPK model in Eqs. (1)–(2) with fixed total \({\text{CLpla}}\) (see Table 6) was used to estimate the parameters \(\sigma _{\text{vas}}\) and \({\text{ABC}}_{\text{tis}}\) of tumour. Parameters are summarised in Table 7. As can be inferred from the Figs. 14 and 15, the tumour has practically no influence on mAb clearance on the time scale of the experimental data.

The rate of change of the interstitial tumour volume \(V_{\text{tum}}(t)\) and the concentration in the interstitial space of the tumour \(C_{\text{tum}}(t)\) was described by the following system of ordinary differential equations (ODEs):

$$\begin{aligned}&\frac{{\text{d}}}{{\text{dt}}}{\text{V}}_{{\text{tum}}} ({\text{t}}) = {\text{k}}_{{\text{growth}}} \cdot {\text{V}}_{{\text{tum}}} ({\text{t}})\\ &\frac{{\text{d}}}{{\text{dt}}}{\text{A}}_{{\text{tum}}} ({\text{t}})={\text{L}}_{{\text{org}}} \cdot \left({(1 - \sigma_{{\text{vas}}}){\text{C}}_{{\text{pla}}} ({\text{t}}) - \frac{{\text{C}}_{{\text{tum}}} ({\text{t}})}{{\text{K}}_{{\text{tum}}}}} \right) - {\text{C}}_{{\text{tum}}} ({\text{t}}) \cdot \frac{{\text{d}}}{{\text{dt}}}{\text{V}}_{{\text{tum}}} ({\text{t}})\end{aligned}$$

from which the concentration was determined by \(C_{\text{tum}}(t)=A_{\text{tum}}(t)/V_{\text{tum}}(t)\).

Table 7 Estimated tumour model parameters for nude and SCID mice
Fig. 14
figure 14

Model fit to experimental data in nude mice based on the PBPK model in Eqs. (1)–(2) with fixed \({\text{CLpla}}\) without tumour compartment (blue solid line) and including a tumour compartment (red dashed line). Fitted mAb concentration-time profiles for nude mice after i.v. bolus administration of 0.0038 mg (dose factor 0.74) mAb MOPC21 compared to experimental data of six mice (filled circles, error bars). Lines represent 50th percentiles (Color figure online)

Fig. 15
figure 15

Model fit to experimental data in SCID mice based on the PBPK model in Eqs. (1)–(2) with fixed \({\text{CLpla}}\) without tumour compartment (blue solid line) and including a tumour compartment (red dashed line). Fitted mAb concentration-time profiles for SCID mice after i.v. bolus administration of mAb 8C2 following two doses (from top to down): 25 mg/kg (dose factor 1) and 1 mg/kg (dose factor 0.58) compared to experimental data of three mice (filled circles). Lines represent the 50th percentiles (Color figure online)

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Fuhrmann, S., Kloft, C. & Huisinga, W. Impact of altered endogenous IgG on unspecific mAb clearance. J Pharmacokinet Pharmacodyn 44, 351–374 (2017). https://doi.org/10.1007/s10928-017-9524-2

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