Influence of body composition on the pharmacokinetics of monoclonal antibodies : example of cetuximab and bevacizumab
Sarah Lobet (1), Adeline Dolly (1), Romain Chautard (1,2), Morgane Caulet (2), Mathilde Cancel (1), Celine Desvignes (3,4), Gilles Paintaud(3,4), Thierry Lecomte (1, 2), David Ternant (3,4)
(1) Tours University, Inserm UMR 1069, Nutrition Croissance et Cancer (N2C), Tours, France ; (2) Department of Gastroenterology and Digestive Oncology, CHRU de Tours, Tours, France; (3) CHRU de Tours, Plateforme Recherche, Centre Pilote de suivi Biologique des traitements par Anticorps (CePiBAc), Tours, France; (4) Tours University, EA4245 Transplantation, Immunologie, Inflammation, Tours, France.
Introduction :
The exposure-response relationship of monoclonal antibodies (mAbs) in oncology is complex and may be blurred by several confounding factors. Cancer-related cachexia has been suggested as the most important of these confounding factors related to disease condition (1,2). Indeed, a high clearance (CL) was associated with shorter survival in several studies. It has been hypothesized that the elevated inflammation and proteolytic activity in cancer-related cachexia are responsible for the increase in CL in patients (3–5). Body composition parameters (muscle and fat mass) are usually quantified using computed tomography (CT) scans in routine clinical practice (6). To date, the impact of body composition on the pharmacokinetics of mAbs has never been assessed using CT scans.
Objective :
This study aimed at investigating the influence of body composition parameters on the pharmacokinetic variability of cetuximab (CTX) and bevacizumab (BV) in metastatic colorectal cancer.
Methods :
Retrospective data from patients routinely treated for metastatic colorectal cancer at the Tours University Hospital were collected over a 15 years period (from 1 January 2006 to 31 December 2021). This study was in accordance with legal requirements and was approved by national and local ethic committees (collection DC2016–2739). Eligible patients had received at least one dose of CTX or BV and for whom plasma concentrations (peaks and through) and CT scans during the follow-up are available.
Serum BV and CTX concentrations were measured by validated enzyme linked immunosorbent assay (ELISA) techniques (7,8). Previous two-compartment TMDD models with quasi-steady-state (QSS) approximation for BV (9) and CTX (10) were used. Model parameters were estimated using Bayesian approach and Monolix software (11).
Total body weight (WT), height (HT) and gender (SEX) were available for all patients and body surface area (BSA) and body mass index (BMI) were calculated using validated equations (12,13). All these parameters were included in the covariate analysis. Baseline CT scans at the level of the 3rd lumbar vertebra (L3) were used to quantify skeletal muscle and adipose tissues cross-sectional areas, using Slice-O-Matic software. These areas were used to estimate fat-free (FFM) and fat (FM) mass, as well as the skeletal muscle index (SMI), using validated methods (6). Body composition parameters were tested as covariates on pharmacokinetic parameters.
Results :
A total of 637 serum CTX concentrations were available in 66 patients and baseline CT scans were available in 63 of these patients (NA=5%). A total of 843 serum BV concentrations were available in 107 patients and baseline CT scans were available of these 100 patients (NA=7%).
The previously developed base models allowed satisfactory concentration-time data and parameter estimates. CTX kinetic parameters (estimate, interindividual standard deviation) were: central (V1=3.1 L, ωV1=0.21) and peripheral (V2=4.2 L ωV2=0.44) volumes of distribution, and systemic (CL = 0.4 L/d, ωCL=0.35) and initial target levels (R0=53 nM, ωR0=0.81). BV kinetic parameters were: central (V1=3.6 L, ωV1=0.26) volumes of distribution, systemic clearance (CL = 0.24 L/d, ωCL=0.38) and initial target levels (R0=1.52 nM, ωR0=0.8).
During univariates analysis, all tested covariates were significantly associated with V1 for both datasets. For CTX, SEX, BSA, FM IMC and WT were significantly associated with CL and FM was significantly associated with R0. For BV, all tested covariates were significantly associated with CL. During the multivariate step, for CTX V1 was significantly increased with BSA (ΔLL=28.98; p=7.10-8) and with SMI (ΔLL=7.37; p=0.006), CL was significantly increased with increasing FM (ΔLL=7.24; p=0.007). For BV, V1 was significantly increased with BSA (ΔLL=29.81; p=5.10-8) and was higher in males (ΔLL=31.81; p=2.10-8). None of body composition parameters were significantly associated with BV PK parameters.
Conclusion :
This is the first study evaluated the impact of body composition on the pharmacokinetics of mAbs. BSA was most strongly associated with PK for both CTX and BV data compared to other body composition parameters. Specific body composition measurements might play a minor role compared to BSA or even WT for CTX.
References :
(1) Dai HI, Vugmeyster Y, Mangal N. Characterizing Exposure-Response Relationship for Therapeutic Monoclonal Antibodies in Immuno-Oncology and Beyond: Challenges, Perspectives, and Prospects. Clin Pharmacol Ther. déc 2020;108(6):1156-70.
(2) Kawakatsu S, Bruno R, Kågedal M, Li C, Girish S, Joshi A, et al. Confounding factors in exposure–response analyses and mitigation strategies for monoclonal antibodies in oncology. British Journal of Clinical Pharmacology. 2021;87(6):2493-501.
(3) Turner DC, Kondic AG, Anderson KM, Robinson AG, Garon EB, Riess JW, et al. Pembrolizumab Exposure-Response Assessments Challenged by Association of Cancer Cachexia and Catabolic Clearance. Clin Cancer Res. 1 déc 2018;24(23):5841-9.
(4) Cohn AL, Yoshino T, Heinemann V, Obermannova R, Bodoky G, Prausová J, et al. Exposure–response relationship of ramucirumab in patients with advanced second-line colorectal cancer: exploratory analysis of the RAISE trial. Cancer Chemother Pharmacol. 1 sept 2017;80(3):599-608.
(5) Liu C, Yu J, Li H, Liu J, Xu Y, Song P, et al. Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis. Clin Pharmacol Ther. mai 2017;101(5):657-66.
(6) Mourtzakis MM, Prado CMMPMM, Lieffers JRLR, Reiman TR, McCargar LJMJ, Baracos VEBE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Applied Physiology, Nutrition, and Metabolism [Internet]. 25 sept 2008 [cité 1 juin 2021]; Disponible sur: https://cdnsciencepub.com/doi/abs/10.1139/H08-075
(7) Cézé N, Ternant D, Piller F, Degenne D, Azzopardi N, Dorval E, et al. An enzyme-linked immunosorbent assay for therapeutic drug monitoring of cetuximab. Ther Drug Monit. oct 2009;31(5):597-601.
(8) Ternant D, Cézé N, Lecomte T, Degenne D, Duveau AC, Watier H, et al. An enzyme-linked immunosorbent assay to study bevacizumab pharmacokinetics. Ther Drug Monit. oct 2010;32(5):647-52.
(9) Sarah Lobet, Morgane Caulet, Nicolas Azzopardi, Gilles Paintaud, Thierry Lecomte, David Ternant. Target-response relationship of bevacizumab may be more relevant than exposure-response - a target-mediated drug disposition (TMDD) model. Disponible sur: PAGE 30 (2022) Abstr 9989 [www.page-meeting.org/?abstract=9989]
(10) Sarah LOBET, Gilles PAINTAUD, Nicolas AZZOPARDI, Christophe PASSOT, Morgane CAULET, Romain CHAUTARD, Celine VIGNAULT-DESVIGNES, Olivier CAPITAIN, David TOUGERON, Michelle BOISDRON-CELLE, Thierry LECOMTE , David TERNANT. Target-mediated pharmacokinetics of cetuximab: target occupancy influences progression-free survival. Disponible sur: PAGE 29 (2021) Abstr 9695 [www.page-meeting.org/?abstract=9695]
(11) Monolix version 2020R1. Antony, France: Lixoft SAS, 2020. http://lixoft.com/products/monolix/.
(12) Mosteller RD. Simplified calculation of body-surface area. N Engl J Med. 22 oct 1987;317(17):1098.
(13) Keys A, Fidanza F, Karvonen MJ, Kimura N, Taylor HL. Indices of relative weight and obesity. J Chronic Dis. 1 juill 1972;25(6):329-43.