Population Pharmacokinetics of Tralokinumab in Adult Subjects With Moderate to Severe Atopic Dermatitis

Anders Soehoel, Malte Selch Larsen, Stine Timmermann, Anders Soehoel, Malte Selch Larsen, Stine Timmermann

Abstract

Tralokinumab is the first biologic therapy for moderate-to-severe atopic dermatitis (AD) that specifically neutralizes interleukin-13 activity, a key driver of AD signs and symptoms. Tralokinumab is a human immunoglobulin G4 monoclonal antibody administered subcutaneously every 2 weeks (with possibility of maintenance dosing every 4 weeks). This population pharmacokinetic (PK) analysis aimed to identify sources of PK variability and relevant predictors of tralokinumab exposure in adults with moderate to severe AD. Nonlinear mixed-effect modeling, including covariate analysis, was used on a data set including 2561 subjects (AD, asthma, healthy) from 10 clinical trials. A 2-compartment model with first-order absorption and elimination adequately described the tralokinumab PK. Body weight was identified as a relevant predictor of tralokinumab exposure; other covariates including age, sex, race, ethnicity, disease type, AD severity, and renal and hepatic impairment were not. For body weight, the difference in exposure between the upper- and lower-weight quartiles in patients with AD was <2-fold, supporting the appropriateness of flat dosing (300 mg). Given the reduced exposure associated with higher body weight, coupled with the reduced exposure provided by dosing every 4 weeks, it is uncertain whether higher-weight patients will achieve sufficient exposure to maintain efficacy if dosed every 4 weeks instead of the standard every 2 weeks.

Trial registration: ClinicalTrials.gov NCT03526861.

Keywords: IL-13; atopic dermatitis; population pharmacokinetics; tralokinumab.

Conflict of interest statement

The authors declare no conflicts of interest.

© 2022 LEO Pharma A/S. Clinical Pharmacology in Drug Development published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.

Figures

Figure 1
Figure 1
Structural representation of the 2‐compartment population pharmacokinetic model of tralokinumab, including first‐order absorption and linear elimination. CL, clearance; F, bioavailability; IV, intravenous; ka, absorption rate constant; Q, intercompartmental clearance; SC, subcutaneous; V2, central volume of distribution; V3, peripheral volume of distribution.
Figure 2
Figure 2
Final model: goodness‐of‐fit plots. Top left: Correlation between the dependent variable (tralokinumab serum concentration) and the population predictions. Top right: Correlation between the dependent variable (tralokinumab serum concentration) and the individual predictions. Bottom left: Correlation between the conditional weighted residuals and the population predictions. Bottom right: Correlation between the conditional weighted residuals and time. The black circles represent the individual observations/predictions/conditional weighted residuals and the red line is the trend line (locally estimated scatterplot smoothing). The black line in the upper panels is the line of unity.
Figure 3
Figure 3
Final model: visual predictive check for weeks 0‐16 in ECZTRA trials (subjects with atopic dermatitis). Visual predictive check (VPC) of the serum concentration‐time profile of tralokinumab in subjects with atopic dermatitis following subcutaneous administration of tralokinumab 300 mg (including loading dose), depicting the observed concentration of tralokinumab (circles), the median of the observed concentration of tralokinumab (solid line), the 95%CI of the simulated median (orange shaded area), the 95%CI of the simulated lower 5th and the upper 95th percentiles (blue shaded areas), and the observed 5th and 95th percentile (dashed line). For visual purposes, serum concentrations

Figure 4

Final model: area under the…

Figure 4

Final model: area under the serum concentration‐time curve (AUC) from week 14 to…

Figure 4
Final model: area under the serum concentration‐time curve (AUC) from week 14 to 16 vs weight quartiles in ECZTRA trials. Boxplot depicting the correlation between individually predicted AUC from week 14 to 16 for all subjects in ECZTRA trials, grouped by weight quartiles (approximative quartiles with n = 361 in Q1, n = 356 in Q2, n = 363 in Q3, n = 350 in Q4). The top, middle, and bottom of each box are the third quartile, median, and the first quartile of data in each category. The whiskers are drawn to the nearest value not beyond 1.5 times the interquartile range (IQR). The circles represent individual values outside 1.5 times the IQR. Only data from subjects who received ≥6 doses of tralokinumab during the first 119 days and who had a PK sample in the time interval 105 to119 days are included in the plot.

Figure 5

Final model simulations. Serum concentration‐time…

Figure 5

Final model simulations. Serum concentration‐time profile for tralokinumab, simulated using the final population…

Figure 5
Final model simulations. Serum concentration‐time profile for tralokinumab, simulated using the final population pharmacokinetic model and based on the following dosing scenario: an initial loading dose of 600 mg at week 0, 300 mg every 2 weeks from week 0 to week 14, and a maintenance dose of 300 mg every 4 weeks from week 16 to week 40 (steady state) for a subject with a body weight of either 50, 75, or 120 kg. For each dose 1000 subjects were simulated and the plots show the 5th, 50th, and 95th percentiles in the population.

Figure 6

Percentage of EASI‐75 responders by…

Figure 6

Percentage of EASI‐75 responders by body weight subgroups in ECZTRA 1 and ECZTRA…

Figure 6
Percentage of EASI‐75 responders by body weight subgroups in ECZTRA 1 and ECZTRA 2 trials. Percentage of responders, defined as at least 75% reduction in Eczema Area and Severity Index (EASI‐75) from baseline to week 16, by body weight subgroups in the clinical trials ECZTRA 1 and ECZTRA 2. All subjects treated with tralokinumab in the initial part of each trial received a dose of 300 mg every 2 weeks. Subjects who received rescue medication were considered nonresponders, and subjects with missing data at week 16 were imputed as nonresponders. Body weight was split into groups by the 25th and 75th percentiles, and the highest body weight group was further split into 101 to 120 kg and >120 kg.
Figure 4
Figure 4
Final model: area under the serum concentration‐time curve (AUC) from week 14 to 16 vs weight quartiles in ECZTRA trials. Boxplot depicting the correlation between individually predicted AUC from week 14 to 16 for all subjects in ECZTRA trials, grouped by weight quartiles (approximative quartiles with n = 361 in Q1, n = 356 in Q2, n = 363 in Q3, n = 350 in Q4). The top, middle, and bottom of each box are the third quartile, median, and the first quartile of data in each category. The whiskers are drawn to the nearest value not beyond 1.5 times the interquartile range (IQR). The circles represent individual values outside 1.5 times the IQR. Only data from subjects who received ≥6 doses of tralokinumab during the first 119 days and who had a PK sample in the time interval 105 to119 days are included in the plot.
Figure 5
Figure 5
Final model simulations. Serum concentration‐time profile for tralokinumab, simulated using the final population pharmacokinetic model and based on the following dosing scenario: an initial loading dose of 600 mg at week 0, 300 mg every 2 weeks from week 0 to week 14, and a maintenance dose of 300 mg every 4 weeks from week 16 to week 40 (steady state) for a subject with a body weight of either 50, 75, or 120 kg. For each dose 1000 subjects were simulated and the plots show the 5th, 50th, and 95th percentiles in the population.
Figure 6
Figure 6
Percentage of EASI‐75 responders by body weight subgroups in ECZTRA 1 and ECZTRA 2 trials. Percentage of responders, defined as at least 75% reduction in Eczema Area and Severity Index (EASI‐75) from baseline to week 16, by body weight subgroups in the clinical trials ECZTRA 1 and ECZTRA 2. All subjects treated with tralokinumab in the initial part of each trial received a dose of 300 mg every 2 weeks. Subjects who received rescue medication were considered nonresponders, and subjects with missing data at week 16 were imputed as nonresponders. Body weight was split into groups by the 25th and 75th percentiles, and the highest body weight group was further split into 101 to 120 kg and >120 kg.

References

    1. Weidinger S, Beck LA, Bieber T, Kabashima K, Irvine AD. Atopic dermatitis. Nat Rev Dis Primers. 2018;4(1):1‐20.
    1. Barbarot S, Auziere S, Gadkari A, et al. Epidemiology of atopic dermatitis in adults: results from an international survey. Allergy. 2018;73(6):1284‐1293.
    1. Silverberg JI, Gelfand JM, Margolis DJ, et al. Patient burden and quality of life in atopic dermatitis in US adults: a population‐based cross‐sectional study. Ann Allergy Asthma Immunol. 2018;121(3):340‐347.
    1. Bieber T. Interleukin‐13: targeting an underestimated cytokine in atopic dermatitis. Allergy. 2020;75(1):54‐62.
    1. Oetjen LK, Mack MR, Feng J, et al. Sensory neurons co‐opt classical immune signaling pathways to mediate chronic itch. Cell. 2017;171(1):217‐228.e213.
    1. Nomura I, Goleva E, Howell MD, et al. Cytokine milieu of atopic dermatitis, as compared to psoriasis, skin prevents induction of innate immune response genes. J Immunol. 2003; 171(6):3262‐3269.
    1. Tsoi LC, Rodriguez E, Degenhardt F, et al. Atopic dermatitis is an IL‐13‐dominant disease with greater molecular heterogeneity compared to psoriasis. J Invest Dermatol. 2019;139(7):1480‐1489.
    1. Szegedi K, Lutter R, Res PC, et al. Cytokine profiles in interstitial fluid from chronic atopic dermatitis skin. J Eur Acad Dermatol Venereol. 2015;29(11):2136‐2144.
    1. Eichenfield LF, Tom WL, Berger TG, et al. Guidelines of care for the management of atopic dermatitis: section 2. Management and treatment of atopic dermatitis with topical therapies. J Am Acad Dermatol. 2014;71(1):116‐132.
    1. Wollenberg A, Barbarot S, Bieber T, et al. Consensus‐based European guidelines for treatment of atopic eczema (atopic dermatitis) in adults and children: part I. J Eur Acad Dermatol Venereol. 2018;32(5):657‐682.
    1. Wollenberg A, Barbarot S, Bieber T, et al. Consensus‐based European guidelines for treatment of atopic eczema (atopic dermatitis) in adults and children: part II. J Eur Acad Dermatol Venereol. 2018;32(6):850‐878.
    1. Blanchard C, Mishra A, Saito‐Akei H, Monk P, Anderson I, and Rothenberg ME. Inhibition of human interleukin‐13‐induced respiratory and oesophageal inflammation by anti‐human‐interleukin‐13 antibody (CAT‐354). Clin Exp Allergy. 2005;35(8):1096‐1103.
    1. May RD, Monk PD, Cohen ES, et al. Preclinical development of CAT‐354, an IL‐13 neutralizing antibody, for the treatment of severe uncontrolled asthma. Br J Pharmacol. 2012;166(1):177‐193.
    1. Thom G, Minter R. Optimization of CAT‐354, a therapeutic antibody directed against interleukin‐13, using ribosome display. Methods Mol Biol. 2012;805:393‐401.
    1. Popovic B, Breed J, Rees DG, et al. Structural characterisation reveals mechanism of IL‐13‐neutralising monoclonal antibody tralokinumab as inhibition of binding to IL‐13Rα1 and IL‐13Rα2. J Mol Biol. 2017;429(2):208‐219.
    1. Wollenberg A, Blauvelt A, Guttman‐Yassky E, et al. Tralokinumab for moderate‐to‐severe atopic dermatitis: results from two 52‐week, randomized, double‐blind, multicentre, placebo‐controlled phase III trials (ECZTRA 1 and ECZTRA 2). Br J Dermatol. 2021;184(3):437‐449.
    1. Silverberg JI, Toth D, Bieber T, et al. Tralokinumab plus topical corticosteroids for the treatment of moderate‐to‐severe atopic dermatitis: results from the double‐blind, randomized, multicentre, placebo‐controlled phase III ECZTRA 3 trial. Br J Dermatol. 2021;184(3):450‐463.
    1. Tollenaere MAX, Litman T, Moebus L, et al. Skin barrier and inflammation genes associated with atopic dermatitis are regulated by interleukin‐13 and modulated by tralokinumab in vitro. Acta Derm Venereol. 2021;101(4):adv00447.
    1. Baverel PG, Jain M, Stelmach I, et al. Pharmacokinetics of tralokinumab in adolescents with asthma: implications for future dosing. Br J Clin Pharmacol. 2015;80(6):1337‐1349.
    1. Oh CK, Faggioni R, Jin F, et al. An open‐label, single‐dose bioavailability study of the pharmacokinetics of CAT‐354 after subcutaneous and intravenous administration in healthy males. Br J Clin Pharmacol. 2010;69(6):645‐655.
    1. Bajaj G, Suryawanshi S, Roy A, and Gupta M. Evaluation of covariate effects on pharmacokinetics of monoclonal antibodies in oncology. Br J Clin Pharmacol. 2019;85(9):2045‐2058.
    1. Dirks NL, Meibohm B. Population pharmacokinetics of therapeutic monoclonal antibodies. Clin Pharmacokinet. 2010;49(10):633‐659.
    1. Tunblad K, Lindbom L, McFadyen L, Jonsson EN, Marshall S, Karlsson MO. The use of clinical irrelevance criteria in covariate model building with application to dofetilide pharmacokinetic data. J Pharmacokinet Pharmacodyn. 2008;35(5): 503‐526.
    1. Karlsson MO, Holford N: A tutorial on visual predictive checks. Abstract 1434, p. 17. Presented at: Annual Meeting of the Population Approach Group in Europe; June 18–20, 2008; Marseille, France.
    1. Mould DR, Sweeney KR. The pharmacokinetics and pharmacodynamics of monoclonal antibodies–mechanistic modeling applied to drug development. Curr Opin Drug Discov Devel. 2007;10(1):84‐96.
    1. Davda JP, Dodds MG, Gibbs MA, Wisdom W, Gibbs J. A model‐based meta‐analysis of monoclonal antibody pharmacokinetics to guide optimal first‐in‐human study design. MAbs. 2014;6(4):1094‐1102.
    1. Garg A, Quartino A, Li J, et al. Population pharmacokinetic and covariate analysis of pertuzumab, a HER2‐targeted monoclonal antibody, and evaluation of a fixed, non‐weight‐based dose in patients with a variety of solid tumors. Cancer Chemother Pharmacol. 2014;74(4):819‐829.
    1. Singh SK. Impact of product‐related factors on immunogenicity of biotherapeutics. J Pharm Sci. 2011;100(2):354‐387.
    1. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2 Suppl 1):S1‐266.
    1. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604‐612.
    1. Mansfield AS, Rudek MA, Vulih D, Smith GL, Harris PJ, Ivy SP, Group NCIODW . The effect of hepatic impairment on outcomes in phase I clinical trials in cancer subjects. Clin Cancer Res. 2016;22(22):5472‐5479.

Source: PubMed

3
S'abonner