2021 - Online - In the cloud

PAGE 2021: Drug/Disease Modelling - Other Topics
Miren Zamacona

Population pharmacokinetic and exposure-response models for dapirolizumab pegol in patients with systemic lupus erythematosus

Chayan Acharya,1 Mats O. Magnusson,1 Pavan Vajjah,2 Miren Zamacona2

1Pharmetheus, Uppsala, Sweden; 2UCB Pharma, Slough, UK


Systemic lupus erythematosus (SLE) is a clinically heterogeneous, systemic, autoimmune disease.[1] Dapirolizumab pegol (DZP) inhibits CD40L and is a polyethylene glycol (PEG)-conjugated antigen-binding (Fab’) fragment lacking a functional Fc domain, which is under clinical development for SLE.



To develop population pharmacokinetic (PK) models of DZP,  in subjects with moderate to severe active SLE. Also, to describe the exposure-response relationship between DZP and BILAG-Based Composite Lupus Assessment (BICLA) responder status.



Plasma concentrations of DZP and the BICLA response status from a placebo controlled, dose ranging, phase 2b clinical study (NCT02804763) informed the model development.[2] The study consisted of a double-blind treatment period in which eligible subjects received standard of care (SOC) plus one of four intravenous treatments (placebo, DZP 6 mg/kg, DZP 24 mg/kg or DZP 45 mg/kg every four weeks) for 24 weeks followed by a further observational period (SOC only) of 24 weeks. Data from both the double-blind and observational periods of this study were included in the analysis. Based on previous knowledge on the pharmacokinetics of DZP,[3] a two-compartment model was the starting point in the population PK analysis. A subpopulation of subjects with persistent active disease or a risk factor for relapsing-remitting disease at baseline was selected for the exposure-response analysis (n=136). The primary efficacy variable, BICLA responder status, was used in the exposure-response modelling longitudinally; this is a dichotomous variable (responder/non-responder) based on a composite index.[4] The BICLA exposure-response relationship was characterised by a mixed effects Markov model describing the probability of transitioning between non-responder, responder and dropout status.[5] The probabilities of transitioning from non-responder to responder and vice versa were estimated, as was the probability of transitioning to treatment dropout given previous responder or non-responder status. Probabilities were parameterised with logit-transformations and interactions with covariates, and DZP exposure was investigated on the logit-scale. The covariates tested included demographics, anti-DZP antibodies, region and disease duration. Non-linear mixed effects modelling was conducted with NONMEM v7.3.0.



DZP PK in SLE patients was dose proportional between 6 mg/kg and 45 mg/kg doses. PK characteristics were well described by a two-compartment model first order elimination from the central compartment. Inter-individual variability was characterised for clearance and central volume of distribution. The only significant covariate identified was body weight on both clearance and volume of distribution. However, body weight explained less than 3% of the interindividual variability for both parameters.

The response model for BICLA responder status included three separate BICLA responder status transition probabilities (non-responder to responder, responder to non-responder and dropout). The placebo response during the double-blind period was modelled using an Emax type of model with PLmax and PLhalf parameters on non-responder to responder transition probability, while the placebo response during the observational period was modelled using a linear function on non-responder to responder transition probability. The treatment effect on BICLA responder status was modelled with a proportional Emax model with DZP average concentration effect on both non-responder to responder and responder to non-responder transition probabilities. Interindividual variability was included in the non-responder to responder and in the responder to non-responder transition probabilities. The half maximal effective concentration (EC50) for the non-responder to responder transition probability was 12 µg/mL while the EC50 for the responder to non-responder transition probability was 150 µg/mL, indicating that higher DZP concentrations may be required to maintain BICLA responder status after 24 weeks of treatment. There were no statistically significant covariates in the BICLA exposure-response model.



The proposed models were able to characterise the DZP PK and the BICLA exposure response in subjects with moderate to severe active SLE. DZP PK was as expected for a PEGylated molecule. A favorable effect of DZP was identified for the BICLA non-responder to responder transition probability and BICLA responder to non-responder transition probability.

[1] Petri M. Best Pract Res Clin Rheumatol 2002;16:847–58; [2] Furie R. et al. Arthritis Rheumatol 2019;71(suppl 10); [3] Tocoian A. et al. Lupus 2015;24:1045–56; [4] Wallace C. et al. Ann Rheum Dis 2014;73:183–190; [5] Lacroix B. et al. Clin Pharmacol Ther 2009;86:387–95.
CA: Employee of Pharmetheus; MM: Employee of Pharmetheus, owns stocks/shares in Pharmetheus; PV: Employee of UCB Pharma; MZ: Employee of UCB Pharma, owns stocks in UCB Pharma.
Funded by UCB Pharma and Biogen. Editorial services provided by Costello Medical.

Reference: PAGE 29 (2021) Abstr 9884 [www.page-meeting.org/?abstract=9884]
Poster: Drug/Disease Modelling - Other Topics