2024 - Rome - Italy

PAGE 2024: Clinical Applications
Jan Berkhout

A model-based approach to predict SC administration and free target dynamics for monoclonal antibodies using PK and total target measurements after IV administration

J. Berkhout (1), D. Fairman (2), Martijn van Noort (1), and T.J. van Steeg (1)

(1) Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), Leiden, The Netherlands, (2) Clinical Pharmacology Modelling and Simulation, GSK, Stevenage, Hertfordshire, United Kingdom

Introduction

The non-linear pharmacokinetics (PK) of monoclonal antibodies (mAbs) can be characterized by target-mediated drug disposition (TMDD) models (1,2). However, developing models that adequately quantify the PK/target engagement (TE)  relationship for mAbs is often challenging (3,4). This analysis was performed using GSK3772847, a human immunoglobulin G2 sigma isotype mAb that binds to the extracellular domain of the interleukin-33 receptor (IL-33R or ST2) and neutralizes IL-33-mediated ST2 signalling, as a model mAb. ST2 is present as both membrane (mST2) and soluble (sST2) forms.

Analytical challenges and biases are present with free target data and total target assays are considered more robust (5–12). This analysis investigated the use of PK and total sST2 data alone to allow prediction of free sST2.
Extensive sampling and a broad IV dose range in a first in human trial (FIH) will deliver optimal data to identify PK parameters and TE dynamics. However, subcutaneous (SC) administration is preferred for most indications. Thus, the utility of using IV data to predict SC PK and TE was also investigated.

 Objectives

  • Evaluate the predictive value of a TMDD model, developed using PK and total sST2 observations only, for free sST2 concentrations.
  • Demonstrate that prediction of SC PK and TE data is feasible, based on typical mAb parameters reported for SC absorption and an existing IV model.

 Methods

For this analysis, data on GSK3772847 PK, free and total sST2 from two phase 1 (13,14) and two phase 2 studies (15,16) was used. In these studies, a total of 322 subjects, either healthy participants or patients, were dosed with GSK3772847 or placebo as single dose (IV or SC) or as multiple dose administration (IV).   

The model for GSK3772847, total and free sST2 was a quasi-steady state (QSS) approximation to a TMDD model (1). This model was developed using PK and total sST2 gathered in the initial IV only FIH trial. The model included linear and saturable clearance (CL) to account for binding and elimination via mST2 (which was not measured directly). The contribution of the saturable clearance pathway diminished over time. Inter-individual variability was identified for CL, the central volume of distribution (V1), and the sST2 concentration at baseline (Bmax). A proportional residual error model was implemented, with separate estimates for PK, and total sST2. The included covariate effects were an increase in CL and V1 with increasing body weight.

The analysis was performed using NONMEM (version 7.4.3) (17), in combination with PsN (version 5.0.0) (18,19). GFortran (version 9.3.0) was used as compiler.

 Results

The phase 1 IV model was applied to the data for PK and total sST2 from a phase 2 study in participants with moderate to severe asthma. The phase 2 data were adequately captured by re-estimating the model parameters, using the phase 1 final parameter values and their standard errors as prior information.
This optimized model, including literature values for absorption rate constant (Ka) of 0.25 day-1 and bioavailability (F1) of 0.77 (20,21), was then used to predict SC data in a subsequent SC study. Good predictive performance was achieved for both PK and total sST2 concentrations.
Next, the TMDD model was optimized based on all PK and total sST2 data. The estimated values for Ka and F1 of, respectively, 0.22 h−1 and 0.67 were near the typical (literature) values. Next to body weight as covariate for CL and V1, a higher Ka when injected at the abdomen compared to upper arm or thigh and a lower Bmax for females compared to males were identified.

Prediction of free sST2 of the final TMDD model, with parameter estimates based on PK and total sST2 observations only, were compared to the measured free sST2 observations. Overall, the trend over time in free sST2 was described accurately across different studies, dose groups and populations.

 Conclusions

This analysis showed that:

  • PK and total target data alone can allow identification and estimation of all the parameters in a QSS approximation of a TMDD model. With free target data being utilised as an additional qualification step.
  • The feasibility of predicting the results of SC administration from IV data alone assuming typical absorption parameters for mAbs. This is an important step to be taken for many mAbs under development as SC is considered favourable for the patient.


Funding: Janssen Research and Development funded study NCT02345928. GSK funded studies NCT03207243, NCT03393806, and NCT04366349 and was involved in all stages of these studies. GSK funded all aspects of the analysis included in this manuscript. GSK also funded all costs associated with the publishing of the manuscript.
References:

  1. Gibiansky L, Gibiansky E, Kakkar T, Ma P. Approximations of the target-mediated drug disposition model and identifiability of model parameters. J Pharmacokinet Pharmacodyn. 2008 Oct;35(5):573–91.
  2. Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J Pharmacokinet Pharmacodyn. 2001 Dec;28(6):507–32.
  3. Gibiansky L, Gibiansky E. Target-mediated drug disposition model: approximations, identifiability of model parameters and applications to the population pharmacokinetic-pharmacodynamic modeling of biologics. Expert Opin Drug Metab Toxicol. 2009 Jul;5(7):803–12.
  4. Gibiansky L, Gibiansky E. Target-mediated drug disposition model for drugs that bind to more than one target. J Pharmacokinet Pharmacodyn. 2010 Aug;37(4):323–46.
  5. Ait-Oudhia S, Lowe PJ, Mager DE. Bridging Clinical Outcomes of Canakinumab Treatment in Patients With Rheumatoid Arthritis With a Population Model of IL-1β Kinetics. CPT pharmacometrics Syst Pharmacol. 2012 Sep;1(9):e5.
  6. Fairman D, Tang H. Best Practices in mAb and Soluble Target Assay Selection for Quantitative Modelling and Qualitative Interpretation. AAPS J. 2023 Feb;25(1):23.
  7. Hood J, González-García I, White N, Marshall L, Dubois VFS, Vicini P, et al. Pharmacokinetic-Pharmacodynamic Modelling of Systemic IL13 Blockade by Monoclonal Antibody Therapy: A Free Assay Disguised as Total. Pharmaceutics. 2021 Apr;13(4).
  8. Kasaian MT, Raible D, Marquette K, Cook TA, Zhou S, Tan X-Y, et al. IL-13 antibodies influence IL-13 clearance in humans by modulating scavenger activity of IL-13Rα2. J Immunol. 2011 Jul;187(1):561–9.
  9. Lee JW, Kelley M, King LE, Yang J, Salimi-Moosavi H, Tang MT, et al. Bioanalytical approaches to quantify “total” and “free” therapeutic antibodies and their targets: technical challenges and PK/PD applications over the course of drug development. AAPS J. 2011 Mar;13(1):99–110.
  10. Pouliquen IJ, Kornmann O, Barton S V, Price JA, Ortega HG. Characterization of the relationship between dose and blood eosinophil response following subcutaneous administration of mepolizumab. Int J Clin Pharmacol Ther. 2015 Dec;53(12):1015–27.
  11. Wang W, Wang X, Doddareddy R, Fink D, McIntosh T, Davis HM, et al. Mechanistic pharmacokinetic/target engagement/pharmacodynamic (PK/TE/PD) modeling in deciphering interplay between a monoclonal antibody and its soluble target in cynomolgus monkeys. AAPS J. 2014 Jan;16(1):129–39.
  12. Zheng S, McIntosh T, Wang W. Utility of free and total target measurements as target engagement and efficacy biomarkers in biotherapeutic development--opportunities and challenges. J Clin Pharmacol. 2015 Mar;55 Suppl 3:S75-84.
  13. Nnane I, Frederick B, Yao Z, Raible D, Shu C, Badorrek P, et al. The first-in-human study of CNTO 7160, an anti-interleukin-33 receptor monoclonal antibody, in healthy subjects and patients with asthma or atopic dermatitis. Br J Clin Pharmacol. 2020 Dec;86(12):2507–18. NCT02345928.
  14. Pefani E, Stone S, Zhu C-Q, Nunn C, Fairman D. Safety, tolerability, and pharmacokinetics of a single ascending subcutaneous dose of GSK3772847 in healthy participants. Pharmacol Res Perspect. 2023 Apr;11(2):e01054. NCT04366349.
  15. Crim C, Stone S, Millar V, Lettis S, Bel EH, Menzies-Gow A, et al. IL-33 receptor inhibition in subjects with uncontrolled asthma: A randomized, placebo-controlled trial. J Allergy Clin Immunol Glob [Internet]. 2022;1(4):198–208. Available from: https://doi.org/10.1016/j.jacig.2022.07.002. NCT03207243.
  16. Akinseye C, Crim C, Newlands A, Fairman D. Efficacy and safety of GSK3772847 in participants with moderate-to-severe asthma with allergic fungal airway disease: A phase IIa randomized, multicenter, double-blind, sponsor-open, comparative trial. PLoS One [Internet]. 2023;18(2 February):3–9. Available from: http://dx.doi.org/10.1371/journal.pone.0281205. NCT03393806.
  17. Beal, S.L., Sheiner, L.B., Boeckmann, A.J. & Bauer RJ. NONMEM 7.4 users guides [Internet]. Available from: https://nonmem.iconplc.com/nonmem743/guides
  18. Lindbom L, Ribbing J, Jonsson EN. Perl-speaks-NONMEM (PsN)--a Perl module for NONMEM related programming. Comput Methods Programs Biomed. 2004 Aug;75(2):85–94.
  19. Lindbom L, Pihlgren P, Jonsson EN. PsN-Toolkit--a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed. 2005 Sep;79(3):241–57.
  20. Quartino AL, Hillenbach C, Li J, Li H, Wada RD, Visich J, et al. Population pharmacokinetic and exposure-response analysis for trastuzumab administered using a subcutaneous “manual syringe” injection or intravenously in women with HER2-positive early breast cancer. Cancer Chemother Pharmacol. 2016 Jan;77(1):77–88.
  21. Wang B, Yan L, Yao Z, Roskos LK, Nnane I, Frederick B, et al. Population Pharmacokinetics and Pharmacodynamics of Benralizumab in Healthy Volunteers and Patients with Asthma. J Allergy Clin Immunol Glob [Internet]. 2017 Dec;6(4):249–57. Available from: https://doi.org/10.1016/j.jacig.2022.07.002

Reference: PAGE 32 (2024) Abstr 10827 [www.page-meeting.org/?abstract=10827]
Poster: Clinical Applications
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