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A PBPK workflow for first-in-human dose selection of a subcutaneously administered pegylated peptide

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Abstract

Predicting the pharmacokinetic (PK) time course of a subcutaneously (SC) administered novel therapeutic protein using in silico approaches offers an opportunity to streamline the drug development process by facilitating selection of starting and target doses in initial human trials. Herein, we propose a workflow for predicting the human exposure time course following SC administration. Leveraging knowledge obtained following both intravenous and SC administration in monkeys, this workflow employs the development of a whole body physiologically-based pharmacokinetic (PBPK) model incorporating vascular circulation, lymphatic uptake and both renal and non-specific clearance mechanisms to predict the PK of a novel pegylated peptide. Optimization of the model was initially performed in monkeys, after which the model was scaled up to human proportion. Inclusion of a SC depot compartment allowed for precise simulation of the SC time course in monkeys. Simulated human exposure after SC administration was within approximately 20 % of the observed values and successfully predicted the time course of two subsequent dosing levels. This workflow represents one of the first publications of a PBPK workflow to predict the time course of a SC administered therapeutic protein based off of a single, non-human primate species and shows promise in facilitating the dose selection in first-in-human dose escalation studies for novel protein therapeutics.

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References

  1. Offman E, Edginton AN (2013) Contrasting toxicokinetic evaluations and interspecies pharmacokinetic scaling approaches for small molecules and biologics: applicability to biosimilar development. Xenobiotica 43:561–569

    Article  CAS  PubMed  Google Scholar 

  2. Ling J, Zhou H, Jiao Q, Davis HM (2009) Interspecies scaling of therapeutic monoclonal antibodies: initial look. J Clin Pharmacol 49:1382–1402

    Article  CAS  PubMed  Google Scholar 

  3. Deng R, Iyer S, Theil FP, Mortensen DL, Fielder PJ, Prabhu S (2011) Projecting human pharmacokinetics of therapeutic antibodies from nonclinical data: what have we learned? Ther Monoclon Antib 3:61–66

    Google Scholar 

  4. Supersaxo A, Hein W, Gallati H, Steffen H (1988) Recombinant human interferon alpha-2a: delivery to lymphoid tissue by selected modes of application. Pharm Res 5:472–476

    Article  CAS  PubMed  Google Scholar 

  5. Supersaxo A, Hein WR, Steffen H (1990) Effect of molecular weight on the lymphatic absorption of water-soluble compounds following subcutaneous administration. Pharm Res 7:167–169

    Article  CAS  PubMed  Google Scholar 

  6. Dahlberg AM, Kaminskas LM, Smith A, Nicolazzo JA, Porter CJ, Bulitta JB, McIntosh MP (2014) The lymphatic system plays a major role in the intravenous and subcutaneous pharmacokinetics of trastuzumab in rats. Mol Pharm 11:496–504

    Article  CAS  PubMed  Google Scholar 

  7. Wang W, Chen N, Shen X, Cunningham P, Fauty S, Michel K, Wang B, Hong X, Adreani C, Nunes CN, Johnson CV, Yin KC, Groff M, Zou Y, Liu L, Hamuro L, Prueksaritanont T (2012) Lymphatic transport and catabolism of therapeutic proteins after subcutaneous administration to rats and dogs. Drug Metab Dispos 40:952–962

    Article  CAS  PubMed  Google Scholar 

  8. Richter WF, Bhansali SG, Morris ME (2012) Mechanistic determinants of biotherapeutics absorption following SC administration. AAPS J 14:559–570

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Rippe B, Haraldsson B (1994) Transport of macromolecules across microvascular walls: the two-pore theory. Physiol Rev 74:163–219

    CAS  PubMed  Google Scholar 

  10. Rippe B, Haraldsson B (1987) Fluid and protein fluxes across small and large pores in the microvasculature. Application of two-pore equations. Acta Physiol Scand 131:411–428

    Article  CAS  PubMed  Google Scholar 

  11. Zhao L, Ji P, Li Z, Roy P, Sahajwalla CG (2013) The antibody drug absorption following subcutaneous or intramuscular administration and its mathematical description by coupling physiologically based absorption process with the conventional compartment pharmacokinetic model. J Clin Pharmacol 53:314–325

    Article  CAS  PubMed  Google Scholar 

  12. Kagan L, Mager DE (2013) Mechanisms of subcutaneous absorption of rituximab in rats. Drug Metab Dispos 41:248–255

    Article  CAS  PubMed  Google Scholar 

  13. Zheng Y, Tesar DB, Benincosa L, Birnbock H, Boswell CA, Bumbaca D, Cowan KJ, Danilenko DM, Daugherty AL, Fielder PJ, Grimm HP, Joshi A, Justies N, Kolaitis G, Lewin-Koh N, Li J, McVay S, O’Mahony J, Otteneder M, Pantze M, Putnam WS, Qiu ZJ, Ruppel J, Singer T, Stauch O, Theil FP, Visich J, Yang J, Ying Y, Khawli LA, Richter WF (2012) Minipig as a potential translatable model for monoclonal antibody pharmacokinetics after intravenous and subcutaneous administration. MAbs 4:243–255

    Article  PubMed Central  PubMed  Google Scholar 

  14. Shah D, Betts A (2012) Towards a platform PBPK model to characterize the plasma and tissue disposition of monoclonal antibodies in preclinical species and human. J Pharmacokinet Pharmacodyn 39:67–86

    Article  CAS  PubMed  Google Scholar 

  15. Davda JP, Jain M, Batra SK, Gwilt PR, Robinson DH (2008) A physiologically based pharmacokinetic (PBPK) model to characterize and predict the disposition of monoclonal antibody CC49 and its single chain Fv constructs. Int Immunopharmacol 8:401–413

    Article  CAS  PubMed  Google Scholar 

  16. 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 

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

    Article  PubMed  Google Scholar 

  18. R Core Team (2013) R: A language and environment for statistical computing

  19. Graf JF, Scholz BJ, Zavodszky MI (2012) BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems. J Pharmacokinet Pharmacodyn 39:37–54

    Article  PubMed Central  PubMed  Google Scholar 

  20. Kawai R, Mathew D, Tanaka C, Rowland M (1998) Physiologically based pharmacokinetics of cyclosporine A: extension to tissue distribution kinetics in rats and scale-up to human. J Pharmacol Exp Ther 287:457–468

    CAS  PubMed  Google Scholar 

  21. Davies B, Morris T (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10:1093–1095

    Article  CAS  PubMed  Google Scholar 

  22. Swartz MA (2001) The physiology of the lymphatic system. Adv Drug Deliv Rev 50:3–20

    Article  CAS  PubMed  Google Scholar 

  23. 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 34:687–709

    Article  CAS  PubMed  Google Scholar 

  24. Baumann A, Tuerck D, Prabhu S, Dickmann L, Sims J (2014) Pharmacokinetics, metabolism and distribution of PEGs and PEGylated proteins: quo vadis? Drug Discov Today 19:1623–1631

    Article  CAS  PubMed  Google Scholar 

  25. Woodburn KW, Fong KL, Wilson SD, Sloneker S, Strzemienski P, Solon E, Moriya Y, Tagawa Y (2013) Peginesatide clearance, distribution, metabolism, and excretion in monkeys following intravenous administration. Drug Metab Dispos 41:774–784

    Article  CAS  PubMed  Google Scholar 

  26. Iwama R, Sato T, Sakurai K, Takasuna K, Ichijo T, Furuhama K, Satoh H (2014) Estimation of glomerular filtration rate in cynomolgus monkeys (Macaca fascicularis). J Vet Med Sci 76:1423–1426

    Article  PubMed Central  PubMed  Google Scholar 

  27. Gordon S (2003) Alternative activation of macrophages. Nat Rev Immunol 3:23–35

    Article  CAS  PubMed  Google Scholar 

  28. Dong JQ, Salinger DH, Endres CJ, Gibbs JP, Hsu CP, Stouch BJ, Hurh E, Gibbs MA (2011) Quantitative prediction of human pharmacokinetics for monoclonal antibodies: retrospective analysis of monkey as a single species for first-in-human prediction. Clin Pharmacokinet 50:131–142

    Article  CAS  PubMed  Google Scholar 

  29. Wang W, Prueksaritanont T (2010) Prediction of human clearance of therapeutic proteins: simple allometric scaling method revisited. Biopharm Drug Dispos 31:253–263

    PubMed  Google Scholar 

  30. Li Z, Rana TM (2014) Therapeutic targeting of microRNAs: current status and future challenges. Nat Rev Drug Discov 13:622–638

    Article  CAS  PubMed  Google Scholar 

  31. Caliceti P, Veronese FM (2003) Pharmacokinetic and biodistribution properties of poly(ethylene glycol)-protein conjugates. Adv Drug Deliv Rev 55:1261–1277

    Article  CAS  PubMed  Google Scholar 

  32. Sarin H (2010) Physiologic upper limits of pore size of different blood capillary types and another perspective on the dual pore theory of microvascular permeability. J Angiogenes Res 2:14

    Article  PubMed Central  PubMed  Google Scholar 

  33. Kusterle M, Jevsevar S, Porekar VG (2008) Size of pegylated protein conjugates studied by various methods. Acta Chim Slov 55:594–601

    CAS  Google Scholar 

  34. Bacher G, Szymanski WW, Kaufman SL, Zollner P, Blaas D, Allmaier G (2001) Charge-reduced nano electrospray ionization combined with differential mobility analysis of peptides, proteins, glycoproteins, noncovalent protein complexes and viruses. J Mass Spectrom 36:1038–1052

    Article  CAS  PubMed  Google Scholar 

  35. Pease LF III, Elliott JT, Tsai DH, Zachariah MR, Tarlov MJ (2008) Determination of protein aggregation with differential mobility analysis: application to IgG antibody. Biotechnol Bioeng 101:1214–1222

    Article  CAS  PubMed  Google Scholar 

  36. Woo S, Jusko WJ (2007) Interspecies comparisons of pharmacokinetics and pharmacodynamics of recombinant human erythropoietin. Drug Metab Dispos 35:1672–1678

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Conflict of interest

No industry funds were received for the novel modeling and simulation aspects of this research. The raw concentration data was gifted to the authors by a party wishing to remain anonymous with written permission to use the data for in silico modeling purposes and publication purposes. EO is a PhD candidate at the School of Pharmacy, University of Waterloo and a paid employee of Celerion, a clinical contract research organization specializing in early human drug development.

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Authors and Affiliations

  1. School of Pharmacy, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada

    Elliot Offman & Andrea N. Edginton

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  1. Elliot Offman
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  2. Andrea N. Edginton
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Correspondence to Andrea N. Edginton.

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Supplementary material 1 (DOCX 29 kb)

Appendices

Appendix 1: representative mass balance equations for a generic PBPK model for SC administration of a pegylated protein conjugate with renal and non-specific clearance

Venous plasma circulation

$$\begin{aligned} V_{\text{ven}} \frac{{C_{{ven}} }}{dt} = & \left( {{\text{Q}}_{\text{heart}} {-}{\text{ L}}_{\text{heart}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{organ}}}} + \left( {{\text{Q}}_{\text{adipose}} {-}{\text{ L}}_{\text{adipose}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{adipose}}}} + \, \left( {{\text{Q}}_{\text{muscle}} {-}{\text{ L}}_{\text{muscle}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{muscle}}}} \\ & + \left( {{\text{Q}}_{\text{kidney}} {-}{\text{ L}}_{\text{kidney}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{kidney}}}} + \, \left( {{\text{Q}}_{\text{bone}} {-}{\text{L}}_{\text{bone}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{bone}}}} + \, \left( {{\text{Q}}_{\text{skin}} {-}{\text{ L}}_{\text{skin}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{skin}}}} + \, \left( {{\text{Q}}_{\text{thymus}} {-}{\text{ L}}_{\text{thymus}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{thymus}}}} \\ & + \left( {{\text{Q}}_{\text{other}} {-}{\text{ L}}_{\text{other}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{other}}}} + \, (\left( {{\text{Q}}_{\text{liver}} {-}{\text{ L}}_{\text{liver}} } \right) \, + \, \left( {{\text{Q}}_{\text{smallintestines}} {-}{\text{ L}}_{\text{smallintestines}} } \right) \, + \, \left( {{\text{Q}}_{\text{largeintestines}} {-}{\text{ L}}_{\text{largeintestines}} } \right) \\ & + \left( {{\text{Q}}_{\text{spleen}} {-}{\text{ L}}_{\text{spleen}} } \right) \, + \, \left( {{\text{Q}}_{\text{pancreas}} {-}{\text{ L}}_{\text{pancreas}} } \right))*{\text{C}}_{{{\text{pla}},{\text{liver}}}} + \, \left( {{\text{L}}_{\text{lymph}} *{\text{C}}_{\text{lymph}} } \right) \, - \, \left( {{\text{Q}}_{\text{lung}} *{\text{C}}_{\text{ven}} } \right) \, {-}{\text{ NRCL}}_{\text{ven}} *{\text{C}}_{\text{ven}} \\ \end{aligned}$$

Arterial plasma circulation

$$\begin{aligned} V_{\text{art}} \frac{{C_{\text{art}} }}{dt} = & \left( {\left( {{\text{Q}}_{\text{lung}} - {\text{L}}_{\text{lung}} } \right)*{\text{C}}_{{{\text{pla}},{\text{lung}}}} } \right) \, {-} \, (({\text{Q}}_{\text{heart}} + {\text{ Q}}_{\text{adipose}} + {\text{ Q}}_{\text{muscle}} + {\text{ Q}}_{\text{kidney}} + {\text{ Q}}_{\text{bone}} + {\text{ Q}}_{\text{thymus}} + {\text{ Q}}_{\text{skin}} + {\text{ Q}}_{\text{brain}} \\ & + {\text{Q}}_{\text{smallintestines}} + {\text{ Q}}_{\text{largeintestines}} + {\text{ Q}}_{\text{spleen}} + {\text{ Q}}_{\text{pancreas}} + {\text{Q}}_{{{\text{other}})}} *{\text{C}}_{\text{art}} ) \, - {\text{ NRCL}}_{\text{art}} *{\text{C}}_{\text{art}} \\ \end{aligned}$$

Lymph

$$V_{\text{lymph}} \frac{{C_{\text{lymph}} }}{dt} = \varSigma \, \left( {{\text{L}}_{\text{organ}} *\left( { 1- \sigma_{\text{isf}} } \right)*{\text{C}}_{{{\text{isf}},{\text{organ}}}} } \right) \, - \, \left( {{\text{L}}_{\text{lymph}} *{\text{C}}_{\text{lymph}} } \right)$$

Lung vascular

$$\begin{aligned} V_{{pla,lung}} \frac{{C_{\text{pla,lung}} }}{dt} = & \left( {{\text{Q}}_{\text{lung}} *{\text{C}}_{\text{ven}} } \right) \, - \, \left( {{\text{L}}_{\text{lung}} *\left( { 1- \left( {\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{lung}}}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{lung}}}} } \right) \, - \, \left( {\left( {{\text{Q}}_{\text{lung}} - {\text{L}}_{\text{lung}} } \right)*{\text{C}}_{{{\text{pla}},{\text{lung}}}} } \right) \\ & - {\text{NRCL}}_{{{\text{pla}},{\text{lung}}}} *{\text{C}}_{{{\text{pla}},{\text{lung}}}} \\ \end{aligned}$$

Lung interstitial

$$V_{\text{isf,lung}} \frac{{C_{{isf, lung}} }}{dt} = \left( {{\text{L}}_{\text{lung}} *\left( { 1- \left( {\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{lung}}}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{lung}}}} } \right) \, - \, ({\text{L}}_{\text{lung}} *( 1- \sigma_{\text{isf}} )*{\text{C}}_{{{\text{isf}},{\text{lung}}}} ) - {\text{NRCL}}_{{{\text{isf}},{\text{lung}}}} *{\text{C}}_{{{\text{isf}},{\text{lung}}}}$$

Kidney vascular

$$\begin{aligned} V_{{pla,kidney}} \frac{{C_{\text{pla, kidney}} }}{dt} = & \left( {{\text{Q}}_{\text{kidney}} *{\text{ C}}_{\text{art}} } \right) - \left( {{\text{L}}_{\text{kidney}} *\left( { 1- \left( {\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{kidney}}}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{kidney}}}} } \right) - \left( {\left( {{\text{Q}}_{\text{kidney}} - {\text{L}}_{\text{kidney}} } \right)*{\text{C}}_{{{\text{pla}},{\text{kidney}}}} } \right) \\ & - \left( {{\text{GFR}}*{\text{FGFR}}*{\text{C}}_{{{\text{pla}},{\text{kidney}}}} } \right) - \left( {{\text{NRCL}}_{{{\text{pla}},{\text{kidney}}}} *{\text{ C}}_{{{\text{pla}},{\text{kidney}}}} } \right) \\ \end{aligned}$$

Kidney interstitial

$$\begin{aligned} V_{{isf,kidney}} \frac{{C_{\text{isf, kidney}} }}{dt} = & \left( {{\text{L}}_{\text{kidney}} *\left( { 1- \left( {\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{kidney}}}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{kidney}}}} } \right) \\ & - \, ({\text{L}}_{\text{kidney}} *( 1- \sigma_{\text{isf}} )*{\text{C}}_{{{\text{isf}},{\text{kidney}}}} ) \, - {\text{NRCL}}_{{{\text{isf}},{\text{kidney}}}} *{\text{C}}_{{{\text{isf}},{\text{kidney}}}} \\ \end{aligned}$$

Skin vascular

$$\begin{aligned} V_{{pla,skin}} \frac{{C_{\text{pla,skin}} }}{dt} = & \left( {{\text{Q}}_{\text{skin}} *{\text{ C}}_{\text{art}} } \right) \, - \, \left( {{\text{L}}_{\text{skin}} + {\text{ L}}_{{{\text{depot}})}} *\left( { 1- \left( {\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{skin}}}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{skin}}}} } \right) \, \\ & - \, \left( {\left( {{\text{Q}}_{\text{skin}} - \left( {{\text{L}}_{\text{skin}} + {\text{ L}}_{\text{depot}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{skin}}}} } \right) \, {-}{\text{ NRCL}}_{{{\text{pla}},{\text{skin}}}} \\ \end{aligned}$$

Skin interstitial

$$V_{\text{isf,skin}} \frac{{C_{{isf, skin}} }}{dt} = ({\text{L}}_{\text{skin}} *( 1- (\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{skin}}}} ))*{\text{C}}_{{{\text{pla}},{\text{skin}}}} ) \, - \, ({\text{L}}_{\text{skin}} *( 1- \sigma_{\text{isf}} )*{\text{C}}_{{{\text{isf}},{\text{skin}}}} ) \, - {\text{ NRCL}}_{{{\text{isf}},{\text{skin}}}} *{\text{C}}_{{{\text{isf}},{\text{skin}}}}$$

Skin subcutaneous depot

$$V_{\text{depot}} \frac{{C_{\text {depot}} }}{dt} = ({\text{L}}_{\text{depot}} *( 1- (\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{skin}}}} ))*{\text{C}}_{\text{depot}} ) \, - \, ({\text{L}}_{\text{depot}} *( 1- \sigma_{\text{isf}} )*{\text{C}}_{\text{depot}} ) \, {-}{\text{ NRCL}}_{\text{depot}} *{\text{C}}_{\text{depot}}$$

Liver vascular

$$\begin{aligned} V_{{pla,liver}} \frac{{C_{\text{pla,liver}} }}{dt} = & \left( {{\text{Q}}_{\text{liver}} * {\text{ C}}_{\text{art}} } \right) \, + \, \left( {\left( {{\text{Q}}_{\text{smallintestines}} - {\text{L}}_{\text{smallintestines}} } \right)*{\text{C}}_{{{\text{pla}},{\text{smallintestines}}}} } \right) \, \\ & + \left( {\left( {{\text{Q}}_{\text{largeintestines}} - {\text{L}}_{\text{largeintestines}} } \right)*{\text{C}}_{{{\text{pla}},{\text{largeintestines}}}} } \right) \, + \, \left( {\left( {{\text{Q}}_{\text{spleen}} - {\text{L}}_{\text{spleen}} } \right) \, * {\text{ C}}_{{{\text{pla}},{\text{spleen}}}} } \right) \\ & + \left( {\left( {{\text{Q}}_{\text{pancreas}} - {\text{L}}_{\text{pancreas}} } \right)*{\text{C}}_{{{\text{pla}},{\text{pancreas}}}} } \right) \, - \, ({\text{L}}_{\text{liver}} * \, ( 1- (\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{liver}}}} ))* {\text{C}}_{{{\text{pla}},{\text{liver}}}} ) \, - \, ((\left( {{\text{Q}}_{\text{liver}} - {\text{L}}_{\text{liver}} } \right) \\ & + \, ({\text{Q}}_{\text{smallintestines}} - {\text{L}}_{\text{smallintestines}} ) \, + \, \left( {{\text{Q}}_{\text{largeintestines}} - {\text{L}}_{\text{largeintestines}} } \right) \, + \, \left( {{\text{Q}}_{\text{spleen}} - {\text{L}}_{\text{spleen}} } \right) \, + \, \left( {{\text{Q}}_{\text{pancreas}} - {\text{L}}_{\text{pancreas}} } \right))*{\text{C}}_{{{\text{pla}},{\text{liver}}}} ) - {\text{ NRCL}}_{{{\text{pla}},{\text{liver}}}} *{\text{C}}_{{{\text{pla}},{\text{liver}}}} \\ \end{aligned}$$

Liver interstitial

$$V_{\text{isf,liver}} \frac{{C_{{isf,liver}} }}{dt} = \, \left( {{\text{L}}_{\text{liver}} * \, \left( { 1- \left( {\sigma_{{{\text{v}},{\text{sf}}}} * \, \sigma_{{{\text{v}},{\text{liver}}}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{liver}}}} } \right) \, - \, ({\text{L}}_{\text{liver}} *( 1- \sigma_{\text{isf}} )*{\text{C}}_{{{\text{isf}},{\text{liver}}}} ) \, - {\text{ NRCL}}_{{{\text{isf}},{\text{liver}}}} *{\text{C}}_{{{\text{isf}},{\text{liver}}}}$$

Remaining organs vascular

$$\begin{aligned} V_{\text {pla,kidney}} \frac{{C_{\text {pla, kidney}} }}{dt} = & \left( {{\text{Q}}_{\text{organ}} *{\text{ C}}_{\text{art}} } \right) \, - \, \left( {{\text{L}}_{\text{organ}} *\left( { 1- \left( {\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{organ}}}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{organ}}}} } \right) \, - \, \left( {\left( {{\text{Q}}_{\text{organ}} - {\text{L}}_{\text{organ}} } \right)*{\text{ C}}_{{{\text{pla}},{\text{organ}}}} } \right) \, \\ & - \, \left( {{\text{GFR}}*{\text{FGFR}}*{\text{C}}_{{{\text{pla}},{\text{organ}}}} } \right) \, - \, \left( {{\text{NRCL}}_{{{\text{pla}},{\text{organ}}}} *{\text{ C}}_{{{\text{pla}},{\text{organ}}}} } \right) \\ \end{aligned}$$

Remaining organs interstitial

$$V_{\text{isf,kidney}} \frac{{C_{\text {isf, kidney}} }}{dt} = \left( {{\text{L}}_{\text{organ}} *\left( { 1- \left( {\sigma_{{{\text{v}},{\text{sf}}}} *\sigma_{{{\text{v}},{\text{organ}}}} } \right)} \right)*{\text{C}}_{{{\text{pla}},{\text{organ}}}} } \right) \, - \, ({\text{L}}_{\text{organ}} *( 1- \sigma_{\text{isf}} )*{\text{C}}_{{{\text{isf}},{\text{organ}}}} ) \, - {\text{NRCL}}_{{{\text{isf}},{\text{organ}}}} *{\text{C}}_{{{\text{isf}},{\text{organ}}}}$$
FGFR:

Fraction of glomerular filtration rate attributed to renal clearance

GFR:

Glomerular filtration rate

ISF (subscripted):

Interstitial space

L:

Lymph flow

NRCL:

Non-renal clearance

Pla (subscripted):

Plasma

Q:

Blood Flow

Vart, Vven :

Volume of the arterial and venous plasma space

Vlymph :

Volume of the lymph node space

Visf,organ :

Volume of the organ interstitial space

Vpla,organ :

Volume of the organ plasma vascular space

σv,sf :

Vascular reflection coefficient, scaling factor

σv,organ :

Organ vascular reflection coefficient

σv,isf :

Interstitial reflection coefficient

Appendix 2

See Table 4 .

Table 4 Monkey and human anatomical and physiological values
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Offman, E., Edginton, A.N. A PBPK workflow for first-in-human dose selection of a subcutaneously administered pegylated peptide. J Pharmacokinet Pharmacodyn 42, 135–150 (2015). https://doi.org/10.1007/s10928-015-9406-4

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