Ling Xue1, Ph.D Xiaoliang Ding1, Prof. Liyan Miao1
1Department of Pharmacy, the First Affiliated Hospital of Soochow University
Objective: Adalimumab (ADAL), a human immunoglobulin G1 monoclonal antibody, has been approved for the treatment of ankylosing spondylitis, a disease characterized by inflammation. Immune response following the administration of ADAL can lead to the generation of anti-drug antibodies (ADA) which may influence the pharmacokinetics of ADAL. The objective of this study is to develop an integrated model to describe the in-vivo kinetics of both ADAL and ADA in patients with ankylosing spondylitis. Methods: A total of 132 patients treated with ADAL for ankylosing spondylitis were enrolled in the present study, yielding 539 trough concentrations of ADAL and 399 concentrations of ADA. The concentration of ADAL was measured using a validated sandwich enzyme-linked immunosorbent assay (ELISA). The concentration of ADA was measured by combining an electrochemiluminescence immunoassay with an immunomagnetic separation strategy. Demographic characteristics and laboratory test results of the patients were recorded from the electronic medical record system. Data analysis was performed using NONMEM. The accuracy and robustness of the model were evaluated through goodness-of-fit plots, bootstrap resampling technique, and visual predictive check. Target concentration intervention (TCI), which is an approach for predicting dose based on the theory of pharmacokinetics (PK) and pharmacodynamics (PD), can use target concentration and individual clearance (CL) to predict the appropriate dose for an individual. The target concentration of ADAL is 5.3 mg/L for Chinese ankylosing spondylitis patients. Results: An integrated model encompasses the dynamic interaction between ADAL bindings to ADA, and the increased clearance via complex mediated pathways, which are integral to the distribution and elimination processes of ADAL. A one-compartment model with first-order absorption and elimination was employed to characterize the pharmacokinetics of ADAL, with the parameter of absorption (Ka) fixed at 0.715 d-1. To describe the in-vivo kinetic process of ADA, a one-compartment model featuring modified transit compartments and first-order elimination was utilized. This modified approach effectively captures the dynamic nature of ADA, which demonstrates a delayed onset followed by intensified responses during subsequent repeated doses, while allowing for a reduction in ADA input following the variable duration of response intensification. The number of transit compartments was fixed at three. The estimated population apparent clearance (CL/F) and apparent volume of distribution (V/F) for ADAL were 0.583 L/d (95% confidence interval (CI): 0.510-0.696 L/d) and 14.1 L (95% CI: 12.0-15.2 L), respectively. The inter-individual variability (IIV) for CL/F and V/F of ADAL were 50.9% and 23.4%, respectively, while the inter-occasion variability (IOV) for these parameters was 31.9% and 7.5%. The proportional residual error and additional residual error for ADAL were 13.9% and 0.417 mg/L, respectively. Body weight was introduced into the CL/F and V/F of ADAL with the allometric scaling model. Notably, the CL/F parameter of for ADAL increased with rising levels of C-reactive protein. The estimated parameters for the ADA input (KinADA), transit rate constant (KTR), elimination rate constant for ADA (KADA), and elimination rate constant for the ADAL-ADA complex (KQSSADA) were 0.528 mg/d (95% CI: 0.399-0.589 mg/d), 0.0512 d-1 (95% CI: 0.041-0.061 d-1), 0.182 d-1 (95% CI: 0.154-0.287 d-1), and 9.32 L/(d*mg*mg) (95% CI: 7.81-12.6 L/(d*mg*mg)), respectively. The corresponding IIV for these parameters were 69.6%, 58.8%, 8.9%, and 139.6%. The proportional residual error and additional residual error for ADA were 50.0% and 0.0515 µg/L, respectively. Conclusions: A dynamic interaction model of ADAL and ADA was established in patients with ankylosing spondylitis. The in-vivo kinetic parameters of ADA were estimated in these patients, which could be used to predict the dynamic profile, and subsequently inform the individual dosage of ADAL. The dose required for an individual could be predicted based on the current model using TCI approach.
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Reference: PAGE 33 (2025) Abstr 11756 [www.page-meeting.org/?abstract=11756]
Poster: Clinical Applications