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Journal of Pharmacokinetics and Pharmacodynamics

, Volume 44, Issue 6, pp 537–548 | Cite as

Platform model describing pharmacokinetic properties of vc-MMAE antibody–drug conjugates

  • Matts Kågedal
  • Leonid Gibiansky
  • Jian Xu
  • Xin Wang
  • Divya Samineni
  • Shang-Chiung Chen
  • Dan Lu
  • Priya Agarwal
  • Bei Wang
  • Ola Saad
  • Neelima Koppada
  • Bernard M. Fine
  • Jin Y. Jin
  • Sandhya Girish
  • Chunze Li
Original Paper

Abstract

Antibody–drug conjugates (ADCs) developed using the valine-citrulline-MMAE (vc-MMAE) platform, consist of a monoclonal antibody (mAb) covalently bound with a potent anti-mitotic toxin (MMAE) through a protease-labile vc linker. Recently, clinical data for a variety of vc-MMAE ADCs has become available. The goal of this analysis was to develop a platform model that simultaneously described antibody-conjugated MMAE (acMMAE) pharmacokinetic (PK) data from eight vc-MMAE ADCs, against different targets and tumor indications; and to assess differences and similarities of model parameters and model predictions, between different compounds. Clinical PK data of eight vc-MMAE ADCs from eight Phase I studies were pooled. A population PK platform model for the eight ADCs was developed, where the inter-compound variability (ICV) was described explicitly, using the third random effect level (ICV), and implemented using LEVEL option of NONMEM 7.3. The PK was described by a two-compartment model with time dependent clearance. Clearance and volume of distribution increased with body weight; volume was higher for males, and clearance mildly decreased with the nominal dose. Michaelis–Menten elimination had only minor effect on PK and was not included in the model. Time-dependence of clearance had no effect beyond the first dosing cycle. Clearance and central volume were similar among ADCs, with ICV of 15 and 5%, respectively. Thus, PK of acMMAE was largely comparable across different vc-MMAE ADCs. The model may be applied to predict PK-profiles of vc-MMAE ADCs under development, estimate individual exposure for the subsequent PK–pharmacodynamics (PD) analysis, and project optimal dose regimens and PK sampling times.

Keywords

Monoclonal antibodies Antibody–drug conjugates (ADCs) vc-MMAE ADCs Population pharmacokinetics 

Notes

Compliance with ethical standards

Conflict of interest

Matts Kågedal, Jian Xu, Xin Wang, Divya Samineni, ShangchiungChen, Dan Lu, Priya Agarwal, Bei Wang, Bernard M. Fine, Jin Y Jin, Sandhya Girish, and Chunze Li are employees of Genentech that provided funding for their research. Leonid Gibiansky is the president of QuantPharm LLC that provides consulting serviced to Genentech.

Supplementary material

10928_2017_9544_MOESM1_ESM.tif (438 kb)
Supplementary material 1 (TIFF 438 kb) Supplemental Fig. 1: Comparison of random effect on clearance versus nominal dose plots of the model with and without dose effect on clearance. Plots of random effect on steady-state clearance versus nominal dose. Bold red line is the lowess trend line across all ADCs. Dashed lines are the the lowess trend lines for the individual ADCs. Top: LEVEL model. Bottom: LEVEL model with dose effect fixed to zero
10928_2017_9544_MOESM2_ESM.tif (593 kb)
Supplementary material 2 (TIFF 593 kb) Supplemental Fig. 2: Diagnostic plots of LEVEL model: observed data (DV) versus LPRED. Red line is a lowess smooth, black line is the line of identity. Each plot indicates each of the individual ADCs. ADC notations (1 to 8) are explained in Table 1
10928_2017_9544_MOESM3_ESM.tif (647 kb)
Supplementary material 3 (TIFF 646 kb) Supplemental Fig. 3: Diagnostic plots of LEVEL model: conditional weighted residuals (CWRES) versus TIME (days). Red line is a lowess smooth. ADC notations (1 to 8) are explained in Table 1
10928_2017_9544_MOESM4_ESM.tif (582 kb)
Supplementary material 4 (TIFF 581 kb) Supplemental Fig. 4: Comparison of simulated concentration-time profiles (median and 95% predictions intervals)
10928_2017_9544_MOESM5_ESM.tif (756 kb)
Supplementary material 5 (TIFF 755 kb) Supplemental Fig. 5: Comparison of diagnostic plots of the models with and without time-dependent clearance. Plots of conditional weighted residual (left) and individual weighted residual (right) versus time. Red line is the lowess trend line. Top: LEVEL model. Bottom: LEVEL model with CLT fixed to zero
10928_2017_9544_MOESM6_ESM.pdf (65 kb)
Supplementary material 6 (PDF 64 kb)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Matts Kågedal
    • 1
  • Leonid Gibiansky
    • 2
  • Jian Xu
    • 1
  • Xin Wang
    • 1
  • Divya Samineni
    • 1
  • Shang-Chiung Chen
    • 1
  • Dan Lu
    • 1
  • Priya Agarwal
    • 1
  • Bei Wang
    • 1
  • Ola Saad
    • 4
  • Neelima Koppada
    • 4
  • Bernard M. Fine
    • 3
  • Jin Y. Jin
    • 1
  • Sandhya Girish
    • 1
  • Chunze Li
    • 1
  1. 1.Clinical PharmacologyGenentech IncSouth San FranciscoUSA
  2. 2.QuantPharm LLCNorth PotomacUSA
  3. 3.Clinical SciencesGenentech IncSouth San FranciscoUSA
  4. 4.BioAnalytical SciencesGenentech IncSouth San FranciscoUSA

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