Development of and insights from systems pharmacology models of antibody-drug conjugates

Abstract

Antibody-drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well-established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC-specific mechanisms and use data-driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far.

FIGURE 1

Key ADC properties and mechanisms for QSP modeling. (a) The antibody, linker, and warhead components of ADCs each have different design properties that must be considered during modeling. Another key characteristic is the drug‐to‐antibody ratio (DAR), which typically varies between one and eight. (b) Key mechanisms of action of the ADC include binding to the target antigen, internalization into the cell, trafficking and recycling of the ADC, endosomal cleavage of the linker or lysosomal degradation of the ADC for warhead release, influx and efflux of the warhead, and cell killing effects at the site of action. ADC, antibody‐drug conjugate; QSP, quantitative systems pharmacology.

FIGURE 2

Structure and key considerations for QSP modeling of ADCs. During QSP modeling of ADCs, the relevant data types may vary between different biological scales, as do the structures of the computational models themselves. Subsequently, the resulting simulations enable the exploration of different phenomena at the in vitro, in vivo, and clinical scales. Ab, antibody; ADC, antibody‐drug conjugate; PBPK, physiologically‐based pharmacokinetic; PK, pharmacokinetic.

FIGURE 3

Characteristics of selected of systems pharmacology models of ADCs. Here, we highlight four examples from the 23 models covered in this review, for which key model characteristics are listed for comparison. In addition to exploring the PK and PD aspects of these models, we will focus on insights gained in four categories as noted on the figure: cellular mechanisms, spatial representation, preclinical translation, and clinical translation. The selected models each contributed significant insights in at least one of these categories, exemplifying the variety of insights that can be gained from QSP modeling. ADC, antibody‐drug conjugate; N/A, not applicable; PBPK, physiologically‐based pharmacokinetic; PD, pharmacodynamic; PK, pharmacokinetic; QSP, quantitative systems pharmacology.