Mechanistic Insights into Cytokine Antagonist-Drug Interactions: A Physiologically Based Pharmacokinetic Modelling Approach with Tocilizumab as a Case Study

Abstract

Background: Understanding interactions between cytokine antagonists and drugs is essential for effective medication management in inflammatory conditions. Recent regulatory authority guidelines emphasise a systematic, risk-based approach to evaluating these interactions, underscoring the need for mechanistic insight. Proinflammatory cytokines, such as interleukin-6 (IL-6), modulate cytochrome P450 (CYP) enzymes, reducing the metabolism of CYP substrates. Cytokine antagonists (such as IL-6 receptor antagonists) can counteract this effect, restoring CYP activity and increasing drug clearance. However, quantitative prediction of cytokine-mediated drug interactions remains challenging, as existing models often lack the mechanistic detail needed to capture the dynamic relationship between cytokine signalling, receptor engagement, and downstream modulation of drug metabolism. Methods: A physiologically based pharmacokinetic (PBPK) framework incorporating cytokine-receptor binding, subsequent downregulation of CYP expression, and blockade of the cytokine signalling by a therapeutic protein antagonist was developed to simulate and investigate cytokine antagonist-drug interactions. Tocilizumab, a humanised IL-6 receptor antagonist used to treat several inflammatory conditions associated with elevated IL-6 levels, was selected as a model drug to demonstrate the utility of the framework. Results: The developed PBPK model accurately predicted the pharmacokinetics profiles of tocilizumab and captured clinically observed dynamic changes in simvastatin exposure before and after tocilizumab treatment in rheumatoid arthritis (RA) patients. Simulated IL-6 dynamics aligned with observed clinical profiles, showing transient elevation following receptor blockade and associated restoration of CYP3A4 activity. Prospective simulations with commonly co-administered CYP substrates (celecoxib, chloroquine, cyclosporine, ibuprofen, prednisone, simvastatin, and theophylline) in RA patients revealed dose regimen- and drug-dependent differences in interaction magnitude. Conclusions: This study demonstrated the utility of PBPK models in providing a mechanistic understanding of cytokine antagonist-drug interactions, supporting enhanced therapeutic decision-making and optimising patient care in inflammatory conditions.

Keywords: PBPK modelling; cytokine antagonist; interleukine-6; therapeutic protein-drug interaction; tocilizumab.

Figure 1

Schematic of simultaneous modelling of TP and co-administered SMDs to predict IL-6R antagonist-drug interactions. The disposition of an IL-6R antagonist (e.g., tocilizumab) is described using a full PBPK model for TPs. In the liver and gut interstitial spaces, endogenous IL-6 binds to mIL-6R, forming IL-6/mIL-6R complexes that mediate the suppression of CYP expression and activity under inflammatory conditions. Upon administration of the IL-6R antagonist, it prevents IL-6 from binding to mIL-6R, reduces IL-6/mIL-6R complex formation, and subsequently alleviates CYP suppression. The restoration of CYP enzyme activity increases the metabolism of co-administered SMDs, resulting in decreased drug exposure. The PK of SMDs is captured using either a minimal or full PBPK model.

Figure 2

Simulated vs. observed plasma concentration–time profiles of tocilizumab in RA patients. Simulated (lines) and observed (data points [12,40,41]) mean plasma concentration–time profiles of tocilizumab following (a) a single IV dose of 10 mg/kg, (b) IV doses of 8 mg/kg Q4W, (c) SC doses of 162 mg Q2W, and (d) SC doses of 162 mg QW. The shaded areas represent the 5th to 95th percentiles of total virtual populations. The error bars represent the standard deviation of the observed data.

Figure 3

Simulated vs. observed plasma concentration-time profiles of simvastatin in RA patients after a single IV administration of tocilizumab (10 mg/kg). Panels show simulated (lines) and observed (data points [12]) mean plasma concentration–time profiles of simvastatin before (left panel), 1 week after (middle panel), and 5 weeks after (right panel) a single IV administration of 10 mg/kg tocilizumab. Simulations were performed using baseline IL-6 concentrations of 50 pg/mL (top row) and 100 pg/mL (bottom row), respectively. The shaded areas represent the 5th to 95th percentiles of total virtual populations. The error bars represent the standard deviation of the observed data.

Figure 4

Simulated time course of cytokine signalling and CYP3A4 regulation in the liver and gut following a single IV administration of tocilizumab (10 mg/kg) in RA patients. Simulated (lines) mean profiles include (a) tocilizumab/mIL-6R complex levels, (b) mIL-6R occupancy by tocilizumab, (c) IL-6/mIL-6R complex levels, (d) free mIL-6R levels, (e) free IL-6 levels, and (f) active CYP3A4 enzyme levels (fold change from baseline) in the liver and gut. Simulations were performed in virtual RA patients with a baseline IL-6 level of 100 pg/mL. Simulated results are shown for the liver (grey) and gut (orange). The shaded areas represent the 5th to 95th percentiles of total virtual populations.

Figure 5

Predicted changes in exposure of co-administered CYP substrate drugs during tocilizumab treatment across multiple dosing regimens. Simulated mean plasma concentration-time profiles of tocilizumab (top row) and corresponding AUC ratios (AUCR, post-/pre-tocilizumab) for co-administered CYP substrate drugs (box plots in rows below) in RA patients receiving tocilizumab 8 mg/kg IV Q4W (left), 162 mg SC Q2W (middle), or 162 mg SC QW (right). In the top row, solid lines represent the population mean and grey shaded areas represent the 5th to 95th percentiles of total virtual populations. Box plots represent simulated AUCR values over a 12-week dosing period for celecoxib, chloroquine, cyclosporine, ibuprofen, prednisone, simvastatin, and theophylline. The central bold line represents the median, box edges represent the 25th and 75th percentiles, and whiskers represent the 5th and 95th percentiles of the total virtual population.

Figure 6

Simulated cyclosporine and theophylline trough concentrations without and with tocilizumab treatment across three clinical regimens. Box plots represent the dynamic changes of predicted trough concentrations for cyclosporine (top row, ng/mL) and theophylline (bottom row, mg/L) in RA patients under four conditions: no tocilizumab treatment and co-administration with tocilizumab treatment of 8 mg/kg IV Q4W, 162 mg SC Q2W, or 162 mg SC QW. The central bold line represents the median, box edges represent the 25th and 75th percentiles, and whiskers represent the 5th and 95th percentiles of the total virtual population. Green shaded areas represent the known therapeutic windows, and red shaded areas and red lines indicate toxicity thresholds [43,44].