Analysis of a Compartmental Model of Endogenous Immunoglobulin G Metabolism with Application to Multiple Myeloma

Abstract

Immunoglobulin G (IgG) metabolism has received much attention in the literature for two reasons: (i) IgG homeostasis is regulated by the neonatal Fc receptor (FcRn), by a pH-dependent and saturable recycling process, which presents an interesting biological system; (ii) the IgG-FcRn interaction may be exploitable as a means for extending the plasma half-life of therapeutic monoclonal antibodies, which are primarily IgG-based. A less-studied problem is the importance of endogenous IgG metabolism in IgG multiple myeloma. In multiple myeloma, quantification of serum monoclonal immunoglobulin plays an important role in diagnosis, monitoring and response assessment. In order to investigate the dynamics of IgG in this setting, a mathematical model characterizing the metabolism of endogenous IgG in humans is required. A number of authors have proposed a two-compartment nonlinear model of IgG metabolism in which saturable recycling is described using Michaelis–Menten kinetics; however it may be difficult to estimate the model parameters from the limited experimental data that are available. The purpose of this study is to analyse the model alongside the available data from experiments in humans and estimate the model parameters. In order to achieve this aim we linearize the model and use several methods of model and parameter validation: stability analysis, structural identifiability analysis, and sensitivity analysis based on traditional sensitivity functions and generalized sensitivity functions. We find that all model parameters are identifiable, structurally and taking into account parameter correlations, when several types of model output are used for parameter estimation. Based on these analyses we estimate parameter values from the limited available data and compare them with previously published parameter values. Finally we show how the model can be applied in future studies of treatment effectiveness in IgG multiple myeloma with simulations of serum monoclonal IgG responses during treatment.

Comparison of MALDI‐TOF mass spectrometry analysis of peripheral blood and bone marrow‐based flow cytometry for tracking measurable residual disease in patients with multiple myeloma

“…A confounding factor is that immunoglobulins, especially IgG and IgA, have a long half‐life in serum (Mills et al , ). This would cause a time lag between tumour lysis and M‐protein elimination, especially at low immunoglobulin concentrations (Kendrick et al , ). The lag may be especially pronounced with the use of new, very potent therapies (Moreau, ).…”

High sensitivity blood-based M-protein detection in sCR patients with multiple myeloma

“…A confounding factor to in our data is the half-life of M-protein in blood (average 21–25 days for IgG and 7–14 days for IgA), which would cause a time lag between tumor lysis and M-protein decrease; an effect which becomes more pronounce at lower total Ig concentrations. 18 Patients with IgA M-proteins and lower M-proteins concentrations pre-ASCT would need a shorter time interval to clear circulating M-protein from blood than IgG M-proteins with high levels pre-ASCT. This was supported by the day 100 results in which the three miRAMM-negative patients were all IgA patients (shorter half-life than IgG) with non-quantifiable M-protein pre-ASCT.…”

A Model‐Based Approach to Quantify the Time‐Course of Anti‐Drug Antibodies for Therapeutic Proteins

“…To our knowledge, this work might be the only one modeling the changes of ADA over time. 11 However, in this 2 20 0 2 4 2 8 2 12 2 16 2 20 study, the ADA titers were collected over 276 weeks and immediately reached the peak level after seroconversion. Consequently, the model focused on the reduction of ADA titers to a new steady state and the half-life was 44 weeks (90% CI: 17-70 weeks).…”

“…Therefore, the typical values of elimination rate constant and lag time to produce ADA M were fixed in our model. Additionally, even if ADA M and ADA G might exhibit profiles with multiphasic elimination curves, 12,13 those related parameters would be hardly estimated accurately and precisely based on current data. In further studies, more detailed data, such as specific ADA M and ADA G titers, would be needed to totally clear their dynamic profiles.…”