Mathematical modeling of receptor occupancy data: A valuable technology for biotherapeutic drug development

Abstract

Background: In drug development, in vivo assessment of target engagement provides confidence when testing the drug's mechanism of action and improves the likelihood of clinical success. For biologics, receptor occupancy (RO) determined from circulating cells can provide evidence of target engagement. Integrating this information with mathematical modeling can further enhance the understanding of drugtarget interactions and the biological factors that are critical to the successful modulation of the target and ultimately the disease state.Methods: This mini-review presents two specific types of mathematical models used to describe antibody-receptor systems and highlights how experimental data can inform the model parameters. Simulations are used to illustrate how various mechanisms influence RO, PK and total cellular receptor profiles.Results: The simulations demonstrate the effect antibody-receptor internalization, affinity and receptor turnover have on commonly acquired data in drug development.Conclusions: Integrating RO data with mathematical models such as the two presented here (targetmediated drug disposition and site-of-action models) can provide a more comprehensive view of the biological system, which can be used to test hypotheses, extrapolate preclinical findings to humans and impact clinical study designs and risk assessments for the successful development of biotherapeutics.

Drug Development of Therapeutic Monoclonal Antibodies

“…The K D of the antibody to the cell receptor is estimated by fitting MFI as a function of the antibody concentration. Integrating receptor occupancy data using mathematical models can provide a means of testing hypotheses, as well as providing a basis for extrapolation of preclinical findings to humans [29].…”

Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity

“…Since the original model was published, a myriad of examples have been reported, showcasing state-of-the-art mathematical models of mAbs exhibiting TMDD. 26 Meijer et al 27 showed that T cell depletion by modulation of their CD3 surface antigens with an anti-CD3 antibody results in a decreased clearance of the mAb at subsequent doses. Ng et al 28 described the PK of TRX1, an anti-CD4 mAb, simultaneously modeling the time-course profiles of free drug exposure and free and total CD4 in healthy subjects.…”

Best practices for optimization and validation of flow cytometry‐based receptor occupancy assays

“…These measurements are particularly important given the prevalence of anti‐drug antibodies in xenogeneic species (Thway et al, 2013). When used as a pharmacodynamic (PD) measurement, ROA data may be combined with pharmacokinetic (PK) data for modeling in order to optimize clinical trial design, guide dose selection, and determine the duration and extent of target saturation (Fisher et al, 2016; Quadrini et al, 2016; Spilker, Singh, & Vicini, 2016; Sternebring et al, 2016; Wyant, Estevam, Yang, & Rosario, 2016). When integrated with anti‐drug‐antibody (ADA) results, ROA data are used to assess the impact of ADA on receptor engagement and to monitor the association of long‐term receptor binding with adverse events.…”

“…clinical trial design, guide dose selection, and determine the duration and extent of target saturation (Fisher et al, 2016;Quadrini et al, 2016;Spilker, Singh, & Vicini, 2016;Sternebring et al, 2016;Wyant, Estevam, Yang, & Rosario, 2016). When integrated with anti-drugantibody (ADA) results, ROA data are used to assess the impact of ADA on receptor engagement and to monitor the association of longterm receptor binding with adverse events.…”