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Review
. 2020 Oct;60 Suppl 1(Suppl 1):S105-S119.
doi: 10.1002/jcph.1720.

Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody-Drug Conjugates

Chunze Li  1 , Shang-Chiung Chen  2 , Yuan Chen  1 , Sandhya Girish  1 , Matts Kaagedal  1 , Dan Lu  1 , Tong Lu  1 , Divya Samineni  1 , Jin Y Jin  1
Affiliations
Review

Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody-Drug Conjugates

Chunze Li et al. J Clin Pharmacol. 2020 Oct.

Erratum in

  • Correction.
    [No authors listed] [No authors listed] J Clin Pharmacol. 2021 Mar;61(3):415. doi: 10.1002/jcph.1816. Epub 2021 Jan 29. J Clin Pharmacol. 2021. PMID: 33524195 Free PMC article. No abstract available.

Abstract

Antibody-drug conjugates are important molecular entities in the treatment of cancer, with 8 antibody-drug conjugates approved by the US Food and Drug Administration since 2000 and many more in early- and late-stage clinical development. These conjugates combine the target specificity of monoclonal antibodies with the potent anticancer activity of small-molecule therapeutics. The complex structure of antibody-drug conjugates poses unique challenges to pharmacokinetic (PK) and pharmacodynamic (PD) characterization because it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (eg, conjugate, total antibody, and unconjugated payload) in different tissues. Quantitative clinical pharmacology using mathematical modeling and simulation provides an excellent approach to overcome these challenges, as it can simultaneously integrate the disposition, PK, and PD of antibody-drug conjugates and their components in a quantitative manner. In this review, we highlight diverse quantitative clinical pharmacology approaches, ranging from system models (eg, physiologically based pharmacokinetic [PBPK] modeling) to mechanistic and empirical models (eg, population PK/PD modeling for single or multiple analytes, exposure-response modeling, platform modeling by pooling data across multiple antibody-drug conjugates). The impact of these PBPK and PK/PD models to provide insights into clinical dosing justification and inform drug development decisions is also highlighted.

Keywords: PBPK; PK/PD; antibody-drug conjugates; drug discovery and development; exposure-response; population PK.

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Conflict of interest statement

All authors are employees of Genentech, Inc. and stockholders of the Roche group.

Figures

Figure 1
Figure 1
Impact of PBPK and PK/PD modeling on the preclinical and clinical development of antibody‐drug conjugates. DDI, drug‐drug interaction; ER, exposure‐response analysis; FIH, first in humans; PBPK, physiologically based pharmacokinetics; PopPK, population pharmacokinetics.
Figure 2
Figure 2
Quantitative clinical pharmacology strategy for antibody‐drug conjugate platform. AE, adverse event; MOA, mechanism of action; Total Ab, total antibody.
Figure 3
Figure 3
PBPK modeling approach to predict the payload‐mediated DDI risk for an antibody‐drug conjugate.
Figure 4
Figure 4
PBPK model of a vc‐MMAE antibody‐drug conjugate. (A) Structure of PBPK model at whole‐body level. Organs are represented by black rectangle and connected by blood flow (red and purple lines) and lymphatic flow (orange dashed line). (B) Structure of PBPK model at tissue level. Each tissue consists of vascular space, endothelial layer, and interstitial space. The distribution of antibody‐drug conjugate and unconjugated MMAE between vascular and interstitial space through convection, diffusion, or transcytosis is simplified and represented by the black double arrow. The formation of unconjugated MMAE from an antibody‐drug conjugate is linked by IgG proteolysis, drug‐to‐antibody ratio‐dependent deconjugation, and drug‐to‐antibody ratio‐dependent plasma clearance (vascular space only).
Figure 5
Figure 5
Population PK model schemes for antibody drug conjugates. (A) Example of total antibody‐acMMAE population PK model (after dosing of polatuzumab vedotin or pinatuzumab vedotin). 25 (B) Example of total antibody‐conjugated antibody population PK model after dosing of T‐DM1 (redrawn based on reference 26). (C) Example of conjugated payload‐unconjugated payload population PK model (after dosing of polatuzumab vedotin). 27 (D) Example of conjugated antibody‐unconjugated payload popPK model after dosing of brentuximab vedotin (redrawn based on reference 28). (E) Example of total antibody‐conjugated payload‐unconjugated payload popPK model (Tab‐acMMAE‐MMAE after dosing of an anti‐CD79b MMAE‐containing antibody‐drug conjugate to cynomolgus monkeys). 18
Figure 6
Figure 6
Time‐to‐event modeling of peripheral neuropathy—platform modeling. 41 (A) Model structure. The hazard in the time‐to‐event model is driven by individually predicted antibody‐drug conjugate plasma concentrations (Cp). Transit and effect compartments were included to account for the slow initial event rate. The hazard was linearly related to the concentration in the effect compartment (Ce). In addition, a Weibull function on top of the drug effect on hazard was added for slight improvement over time. Covariates in the full and final models were included assuming proportional hazards (Prop HZ). (B) Estimated hazard ratios (95%CI) for covariate effects based on full and final model. ALBU, albumin; BWT, body weight; CI, confidence interval; Ctransit, concentration in the transit compartment; ECOG, Eastern Cooperative Oncology Group; Ktr, first‐order transit rate; PN, peripheral neuropathy.
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