David TERNANT1, Olivier LE TILLY1, Laurence PICON2, Thierry LECOMTE3, Denis MULLEMAN4, Philippe GOUPILLE4, Christophe PASSOT5, Gilles PAINTAUD1
1. Université de Tours, EA 4245 «Transplantation, Immunology, Inflammation», Tours, France; CHRU de Tours, Department of Clinical Pharmacology, Tours, France 2. CHRU de Tours, Department of Gastroenterology, Tours, France 3. Université de Tours, UMR INSERM U1069 N2C, Tours, France; CHRU de Tours, Department of Gastroenterology, Tours, France 4. Université de Tours, EA 7501 « Groupe Innovation et Ciblage Cellulaire », Tours, France; CHRU de Tours, Department of Rheumatology, Tours, France 5. Département de Biopathologie, Institut de Cancérologie de l’Ouest, Angers, France
Objectives: Infliximab is an anti-TNF monoclonal antibody approved in chronic inflammatory bowel diseases (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC). Up to date, in IBD, the pharmacokinetics of infliximab was reported in 25 publications that used compartmental modeling [1]. Surprisingly, the inter-publication variability of pharmacokinetic parameter estimates was very large, which could be damaging since some of these models were proposed as dosing optimization in clinical practice [2,3]. This variability may be due to complex target-mediated pharmacokinetics that has been investigated in only a few studies [4]. This study aimed at providing a sound description of infliximab target-mediated pharmacokinetics using target-mediated drug disposition (TMDD) modeling.
Methods: Infliximab pharmacokinetics was investigated using two databases in which patients were treated with 5 mg/kg at weeks 0, 2, 6, 14 and 22: (i) a retrospective cohort of 132 IBD patients [5] for whom trough and peak infliximab concentrations were determined from samples collected between weeks 0 and 22 after treatment initiation, and (ii) a prospective trial of 26 ankylosing spondylitis patients [6] for which blood samples were collected before, and 2 and 4 jours after each infusion, and at each intermediate visit (weeks 1, 3, 4, 5, 8, 10 and 14). A two-compartment model was used to describe the kinetics of unbound infliximab. In IBD patients, infliximab pharmacokinetics was investigated using TMDD modeling and quasi-steady-state approximation. Infliximab was assumed to bind to TNF in both central and peripheral compartments, and to present no target-mediated elimination in AS, which was used as a reference. Modeling was assessed using the nonlinear mixed-effect model software Monolix (Lixoft®, Antony, France).
Results: A total of 1333 concentration measurements were available in the 158 patients. Concentration-time data were satisfactory described using TMDD model with TNF interaction in both central and peripheral compartments. Parameter estimates of unbound infliximab kinetics were central (V1=2.6 L) and peripheral (V2=1.9 L) volumes of distribution and systemic (CL=0.16 L/day) and intercompartment (Q=2.0 L/day) clearances. Target-mediated parameters respectively of central and peripheral compartments were baseline TNF concentrations (cR0 = 3.4 nM and pR0 = 0.39 nM), steady-stated dissociation rates (cKSS = 11.1 nM and pKSS = 0.42 nM) and first-order elimination rates of complexes (ckint = 0.15 day-1 and (pkint = 0.0058 day-1); first-order elimination rate of TNF was hardly identifiable and was fixed to 20 day-1 in both compartments. Interindividual standard deviations could be estimated for V1 (0.27), CL (0.26), V2 (0.36), cR0 (0.97) and pR0 (1.0). This model showed large differences in target turnover and interaction with infliximab between central and peripheral compartments and displayed a pseudo-triphasic elimination shape for infliximab concentrations.
Conclusions: This study is the first to show the large complexity in infliximab pharmacokinetics in IBD patients. Given this complexity, the difference in parameter estimates reported in previous publications may be partly due to sampling strategies. This model may be useful to compare these results and to propose an harmonized pharmacokinetic model for concentration prediction in routine practice.
References:
[1] Bensalem A, Ternant D. Pharmacokinetic Variability of Therapeutic Antibodies in Humans: A Comprehensive Review of Population Pharmacokinetic Modeling Publications. Clin Pharmacokinet 2020, 59:857-874.
[2] Eser A, Primas C, Reinisch S, Vogelsang H, Novacek G, Mould DR, Reinisch W. Prediction of individual serum infliximab concentrations in inflammatory bowed disease by a Bayesian dashboard system. J Clin Pharmacol 2018, 58:790-802.
[3] Xiong Y, Mizuno T, Colman R, Hyams J, Noe JD, Boyle B, Tsai YT, Dong M, Jackson K, Punt N, Rosen MJ, Denson LA, Vinks AA, Minar P. Real-World Infliximab Pharmacokinetic Study Informs an Electronic Health Record-Embedded Dashboard to Guide Precision Dosing in Children with Crohn’s Disease. Clin Pharmacol Ther 2020, cpt.2148
[4] Berends SE, van Steeg TJ, Ahsman MJ, Singh S, Brandse JF, D’Haens GRAM, Mathôt RAA. Tumor necrosis factor-mediated disposition of infliximab in ulcerative colitis patients. J Pharmacokinet Pharmacodyn 2019, 46:543-551.
[5] Aubourg A, Picon L, Lecomte T, Bejan-Angoulvant T, Paintaud G, Ternant D. A robust estimation of infliximab pharmacokinetic parameters in Crohn’s disease. Eur J Clin Pharmacol 2015, 71:1541-1542.
[6] Ternant D, Mulleman D, Lauféron F, Vignault C, Ducourau E, Wendling D, Goupille P, Paintaud G. Influence of methotrexate on infliximab pharmacokinetics and pharmacodynamics in ankylosing spondylitis. Br J Clin Pharmacol 2012, 73:55-65.
Reference: PAGE 29 (2021) Abstr 9678 [www.page-meeting.org/?abstract=9678]
Poster: Drug/Disease Modelling - Other Topics