Sergei Vavilov 1, Joel Zirkle 1, Elin Boger 1, Teodor Erngren 1, Nina Lawrence 1
1 Drug Metabolism and Pharmacokinetics, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca (Gothenburg, Sweden)
Objectives:
Current understanding of pathogenesis and treatment of inflammatory bowel disease (IBD) remains incomplete, because the disease progression is controlled by the interplay of multiple competing cytokines and cells in a heterogenous patient population (1). Quantitative systems pharmacology (QSP) models aim to describe these interactions mechanistically. A previously published QSP model of IBD (1) integrates a mechanistic description of inflammation pathways (in the form of coupled ordinary differential equation system that includes 49 species of cytokines and immune cells in plasma and colon compartments) and a virtual population of individual parameter values. This model was able to describe the response to ustekinumab, risankizumab, brazikumab and infliximab (2), but lacks the pathways needed to describe the effect of other approved IBD treatments, including vedolizumab, tofacitinib or ozanimod.
Vedolizumab is a monoclonal antibody that binds to the integrin α4β7 transmembrane proteins on the surface of T cells, preventing the integrin from binding to colon-specific MAdCAM-1 receptors and decreasing the infiltration of T cells into the colon (3). It has been shown to improve C-reactive protein and fecal calprotectin levels in IBD patients (4), including those that did not respond to tumour necrosis factor antagonist treatment. There is evidence that vedolizumab binding to Treg cells does not affect their suppressive activity, quickly internalizes with the integrin, and functional integrins reappear on the surface of the cells (5).
Methods:
A published QSP model (1) of IBD was extended with a pathway describing vedolizumab binding to the integrin α4β7 proteins on the surface of T cells, impacting the T cell trafficking into the gut compartment. A published pharmacokinetic (PK) model was used for vedolizumab (6), and in vitro binding data were used to quantify the binding between vedolizumab and integrin α4β7 (7). Data on T cell population in the colon (8) was used to calibrate the binding reactions between vedolizumab and integrin proteins on the surface of T cells.
Results:
The model was extended to contain 52 species, 343 parameters and 119 reactions. A virtual population of patients was used to describe the variation in baseline cytokine and biomarker levels. MATLAB SimBiology 2025b was used to run the simulations, predict antibody PK, integrin expression, T cell infiltration and biomarker levels. The model was calibrated against published data on plasma and colon concentration of pro-inflammatory and anti-inflammatory cytokines, immune cell concentration and biomarkers of disease progression. The addition of the integrin α4β7 pathway did not impact the baseline predictions of the model. A relationship between vedolizumab-induced changes in T cell counts in the colon and resulting levels of C-reactive protein and fecal calprotectin was identified for subpopulations of patients that matched the baseline characteristics of study (4).
Conclusions:
Extending the QSP model of IBD allows to better describe the heterogeneous population of IBD patients and the efficacy, or lack of efficacy, in the patient population for drugs that affect the immune system, such as cytokine-targeting drugs. These predictions can be used in early drug discovery, including target validation and lead optimization, and throughout the development of therapeutic drugs for IBD.
References:
1. Rogers, K.V. et al. (2021), A Dynamic Quantitative Systems Pharmacology Model of Inflammatory Bowel Disease: Part 1 – Model Framework. Clin Transl Sci, 14: 239-248.
2. Rogers, K.V. et al. (2021), A Dynamic Quantitative Systems Pharmacology Model of Inflammatory Bowel Disease: Part 2 – Application to Current Therapies in Crohn’s Disease. Clin Transl Sci, 14: 249-259.
3. Crooks B, Barnes T, Limdi JK. Vedolizumab in the treatment of inflammatory bowel disease: evolving paradigms. Drugs Context. 2020 Mar 2;9:2019-10-2.
4. Bruce E. Sands et al., Effects of Vedolizumab Induction Therapy for Patients With Crohn’s Disease in Whom Tumor Necrosis Factor Antagonist Treatment Failed, Gastroenterology, Volume 147, Issue 3, 2014, Pages 618-627.e3, ISSN 0016-5085.
5. Wyant T, Yang L, Fedyk E. In vitro assessment of the effects of vedolizumab binding on peripheral blood lymphocytes. MAbs. 2013 Nov-Dec;5(6):842-50.
6. Rosario M et al. Population pharmacokinetics-pharmacodynamics of vedolizumab in patients with ulcerative colitis and Crohn’s disease. Aliment Pharmacol Ther. 2015 Jul;42(2):188-202.
7. Dulce Soler et al., The Binding Specificity and Selective Antagonism of Vedolizumab, an Anti-α4β7 Integrin Therapeutic Antibody in Development for Inflammatory Bowel Diseases, The Journal of Pharmacology and Experimental Therapeutics, Volume 330, Issue 3, 2009, Pages 864-875, ISSN 0022-3565.
8. Veny M et al. Dissecting Common and Unique Effects of Anti-α4β7 and Anti-Tumor Necrosis Factor Treatment in Ulcerative Colitis. J Crohns Colitis. 2021 Mar 5;15(3):441-452.
Reference: PAGE 34 (2026) Abstr 11879 [www.page-meeting.org/?abstract=11879]
Poster: Drug/Disease Modelling - Other Topics