Charlotte M Thomas (1, 2), Dr Teresa Tsakok (3), Professor Jonathan Barker (1), Professor RB Warren (4, 5), Dr Sam Norton (6, 7), Zehra Arkir (8), Monica Arenas-Hernandez (9), Professor Catherine H Smith (1, 3), Professor Joseph F Standing (2), Dr Satveer K Mahil (1, 3)
(1) St John's Institute of Dermatology, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom, (2) Infection, Immunity and Inflammation Research & Teaching Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom, (3) St John's Institute of Dermatology, Guy's and St Thomas' National Health Service (NHS) Foundation Trust, London, United Kingdom, (4) Dermatology Centre, Northern Care Alliance NHS Foundation Trust, Manchester, United Kingdom, (5) NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom, (6) Health Psychology Section, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom, (7) Centre for Rheumatic Diseases, London, United Kingdom, (8) Reference Chemistry, Synnovis, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom, (9) Viapath Pathology Services, Guy's and St Thomas' Hospital NHS Foundation Trust, London, United Kingdom
Objectives:
Risankizumab is an anti-interleukin (IL)-23 monoclonal antibody used in the treatment of plaque psoriasis. We sought to develop a pharmacokinetic-pharmacodynamic (PKPD) model to investigate the relationship between risankizumab exposure and treatment response, assessed by Psoriasis Area Severity Index (PASI), in psoriasis patients with the potential to personalise dosing strategies.
Methods:
The study utilised a real-world dataset from the Biomarkers and Stratification To Optimise outcomes in Psoriasis (B-STOP) study, comprising 93 serum observations from 50 patients (54% male; median weight 79.3 kg; median age 47 years). Patients received standard dosing of Risankizumab (150mg subcutaneously at weeks 0, 4, and every 12 thereafter). A sequential population pharmacokinetic-pharmacodynamic model was constructed using a non-linear mixed effect modelling approach in R (version 4.3.1) with the nlmixr2 package (version 2.0.9). Data below the limit of quantification (1 mg/L) were handled using the M3 method [1]. Covariates including albumin, creatinine, creatinine clearance, and C-reactive protein were assessed in the development of the covariate model. Allometric scaling was incorporated into the model as a prior centred around 70 kg, and maximum inhibition (Imax) was fixed to 1.
Results:
Pharmacokinetic data was best described by a one-compartment model with a fixed absorption rate (Suleiman et. al ka = 0.299) [2], with first‐order subcutaneous absorption and first‐order elimination. The final model accounted for inter-individual variability (IIV) on clearance (CL) and volume (V). Residual variability was best described using a proportional error model (0.5). A maximum effect (Emax) model inhibited progression of psoriatic skin lesions, utilising a turnover pharmacodynamic mechanism to describe PASI evolution while on treatment, where kin represented lesion development and kout represented the elimination rate constant of lesions. Inclusion of IIV on baseline PASI score (BSL) and concentration at 50% of maximum inhibition (IC50) with a combined proportional (0.7) and additive error (0.01) model best described the pharmacodynamic data. No covariates were retained in the model. CL/F was estimated at 0.3 L/day, V/F at 11.8 L, BSL at 12.7, IC50 at 0.1 mg/L and kout at 0.03 day-1.
Conclusions:
Pharmacokinetic parameters were similar to those of previously published risankizumab models [2]. The pharmacodynamic model showcased lesion turnover consistent with other turnover models for psoriatic skin lesions, highlighting uniform turnover dynamics across different drugs [3]. A bespoke Bayesian therapeutic drug monitoring dashboard is under development to allow for real-time dose interval estimation for optimising the use of risankizumab in patients with psoriasis. The PKPD model will be remodelled as a bounded integer model and updated as new data becomes available. Eventually, the dashboard will be expanded to include multiple biologics used in the treatment of psoriasis.
References:
[1] S L Beal. Ways to fit a PK model with some data below the quantification limit. J. Pharmacokinet. Pharmacodyn., 28(5):481–504, October 2001.
[2] Ahmed A Suleiman, Mukul Minocha, Amit Khatri, Yinuo Pang, and Ahmed A Othman. Populationpharmacokinetics of risankizumab in healthy volunteers and subjects with moderate to severe plaque psoriasis: Integrated analyses of phase I-III clinical trials. Clin. Pharmacokinet., 58(10):1309–1321, October 2019.
[3] Shan Pan, Teresa Tsakok, Nick Dand, Dagan O Lonsdale, Floris C Loeff, Karien Bloem, Annick de Vries, David Baudry, Michael Duckworth, Satveer Mahil, Angela Pushpa-Rajah, Alice Russell, Ali Alsharqi, Gabrielle Becher, Ruth Murphy, Shyamal Wahie, Andrew Wright, Christopher E M Griffiths, Nick J Reynolds, Jonathan Barker, Richard B Warren, A David Burden, Theo Rispens, Joseph F Standing, Catherine H Smith, and BADBIR Study Group, the BSTOP Study Group, the PSORT Consortium. Using real-world data to guide ustekinumab dosing strategies for psoriasis: A prospective pharmacokinetic-pharmacodynamic study. Clin. Transl. Sci., 13(2):400–409, March 2020.
Reference: PAGE 32 (2024) Abstr 11146 [www.page-meeting.org/?abstract=11146]
Poster: Drug/Disease Modelling - Other Topics